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Nonparametric permutation tests for functional neuroimaging: A primer with examples

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Abstract

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.

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... The TICe allows the screening of variables because of its high power, but low equitability and the MICe estimates the strengths of the relationships because of its high equitability but lower power. In addition, speech variables and clinical scores correlations were corrected for multiple comparisons with the Maximum Statistic correction to take into account the correlations between the variables [48]. ...
... Forward speech features obtained for the different scores: cUHDRS MAE = 2.6 ± 0.5; TMS MAE = 11.7 ± 1.8; TFC MAE = 1.5 ± 0.2; (Table 3). Comparison correction was performed with the Maximum Statistic [48]. The Mean duration of Silences obtained the strongest strength of relationship based on the MICe , while the cUHDRS obtained the strongest linear relationship with the Pearson coefficient R. ...
... Summary of the speech and clinical variables with significant correlation with the Normalized Volume of the StriatumThe comparison between the TICe 's P values[46], the measure of linear relationship with the Pearson R coefficient, the Spearman rank correlation coefficient , the measure of strength of the relationship with the MICe shows that Mean duration of Silences and the Standard Deviation of the duration of Silences are as well correlated with the striatal volume than the regular clinical scores. Multiple Comparison correction is done with the Maximum Statistic[48] ...
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Objectives Using brief samples of speech recordings, we aimed at predicting, through machine learning, the clinical performance in Huntington’s Disease (HD), an inherited Neurodegenerative disease (NDD). Methods We collected and analyzed 126 samples of audio recordings of both forward and backward counting from 103 Huntington’s disease gene carriers [87 manifest and 16 premanifest; mean age 50.6 (SD 11.2), range (27–88) years] from three multicenter prospective studies in France and Belgium (MIG-HD (ClinicalTrials.gov NCT00190450); BIO-HD (ClinicalTrials.gov NCT00190450) and Repair-HD (ClinicalTrials.gov NCT00190450). We pre-registered all of our methods before running any analyses, in order to avoid inflated results. We automatically extracted 60 speech features from blindly annotated samples. We used machine learning models to combine multiple speech features in order to make predictions at individual levels of the clinical markers. We trained machine learning models on 86% of the samples, the remaining 14% constituted the independent test set. We combined speech features with demographics variables (age, sex, CAG repeats, and burden score) to predict cognitive, motor, and functional scores of the Unified Huntington’s disease rating scale. We provided correlation between speech variables and striatal volumes. Results Speech features combined with demographics allowed the prediction of the individual cognitive, motor, and functional scores with a relative error from 12.7 to 20.0% which is better than predictions using demographics and genetic information. Both mean and standard deviation of pause durations during backward recitation and clinical scores correlated with striatal atrophy (Spearman 0.6 and 0.5–0.6, respectively). Interpretation Brief and examiner-free speech recording and analysis may become in the future an efficient method for remote evaluation of the individual condition in HD and likely in other NDD.
... Data were pre-processed and analyzed using FMRIB's Software Library (FSL 6.0.4) and SPM12 (Neuroimaging, 2020) and CONN Functional Connectivity Toolbox (NITRC, 2020) All T1-weighted images were corrected for field bias using N4BiasFieldCorrection, then were segmented into grey matter, white matter, and cerebrospinal fluid using the SPM-unified segmentation pipeline. The SPM-DARTEL pipeline was used to generate a study-specific template and normalize structural data to the template using field warp and grey matter deformation maps that can be used to normalize the structural and fMRI data into common space. ...
... To explicate the effect of the mindfulness intervention on FC, voxel-based analysis of the different brain networks was performed to estimate differences between pre-and postintervention FC. Differences were estimated by entering individual maps into a general linear model; paired t-tests were performed with permutation testing (FSL-randomize; 5000 permutations) (Nichols & Holmes, 2002). All statistical analyses were corrected for age and gender. ...
... All statistical analyses were corrected for age and gender. Results were corrected for multiple comparisons with family-wise error (p ≤ 0.05, cluster > 100 voxels) (Nichols & Holmes, 2002). In addition, we performed correlations between FC maps and cognitive variables SCAS total, detectability commissions and hit response time at pre-and post-intervention timepoints (significance at p ≤ 0.05). ...
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Mindfulness training has been associated with improved attention and affect regulation in preadolescent children with anxiety related attention impairments, however little is known about the underlying neurobiology. This study sought to investigate the impact of mindfulness training on functional connectivity of attention and limbic brain networks in pre-adolescents. A total of 47 children with anxiety and/or attention issues (aged 9-11 years) participated in a 10-week mindfulness intervention. Anxiety and attention measures and resting-state fMRI were completed at pre- and post-intervention. Sustained attention was measured using the Conners Continuous Performance Test, while the anxiety levels were measured using the Spence Children’s Anxiety Scale. Functional networks were estimated using independent-component analysis, and voxel-based analysis was used to determine the difference between the time-points to identify the effect of the intervention on the functional connectivity. There was a significant decrease in anxiety symptoms and improvement in attention scores following the intervention. From a network perspective, the results showed increased functional connectivity post intervention in the salience and fronto-parietal networks as well as the medial-inferior temporal component of the default mode network. Positive correlations were identified in the fronto-parietal network with Hit Response Time and the Spence Children’s Anxiety Scale total and between the default mode network and Hit Response Time. A 10-week mindfulness intervention in children was associated with a reduction in anxiety related attention impairments, which corresponded with concomitant changes in functional connectivity.
... The main difference between BOLD signal series with ordinary time-series data is that BOLD is a group of aligned sequences instead of independent ones. Traditional BOLD encoder methods like ICA (Tuovinen et al., 2017) and PCA (Thomas et al., 2002) ignore the temporal order. Also, PCA and ICA can only capture linear information within or across time series. ...
... There are also findings that reveal gender differences in FC between brain regions (Satterthwaite et al., 2015). To investigate FC alterations in demographic and clinical subpopulations, the commonly adopted methods include edge-wise tests for between-group differences (Nichols and Holmes, 2002;Chen et al., 2015;Kim et al., 2015), tests for detecting coordinated disruptions across multiple brain subsystems (Zalesky et al., 2010;Higgins et al., 2019), graph theory based methods for comparing brain network graph metrics (Rudie et al., 2013;Rubinov and Sporns, 2010;Fornito et al., 2013) and graphical model approaches (Lukemire et al., 2021;Higgins et al., 2018;. ...
Preprint
Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.}
... Assortativity coefficient of the network ( Newman, 2002 ): Single-slice modularity of the network ( Newman, 2004 ): ...
... For mesoscale and correlation analyses, we used one-way permutation tests to examine the difference between sessions (i.e., morning and evening) and detect correlations' significance, respectively. This approach does not require distributional assumptions ( Nichols and Holmes, 2002 ). A false discovery rate (FDR) correction was applied to all statistical tests ( Benjamini and Hochberg, 1995 ). ...
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Circadian rhythms (lasting approximately 24 hours) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 hours (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments.
... Therefore, although a significant EDI does not preclude the possibility that the effect might be driven by some set of conditions or not present in all subjects, the tools we provide here allows clarification in full depth. (Nichols & Holmes, 2002). For reasons that we explain in the paper, the null distribution of the EDI is likely to violate the required assumptions of the t test (e.g. ...
... 4.3 Testing exemplar information at the single-subject level or group-level with subject as fixed effect is possible using novel randomization tests Randomization tests, and more generally non-parametric statistics, are becoming more and more popular in the univariate analysis of neuroimaging data (Nichols and Holmes, 2002). We can also use randomization tests and non-parametric statistics in the context of testing for information in brain response-patterns. ...
Preprint
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Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of brain activity have used condition-rich designs to sample the stimulus space more densely. To test whether brain response patterns discriminate among a set of stimuli (e.g. exemplars within a category) with good sensitivity, we can pool statistical evidence over all pairwise comparisons. Here we describe a wide range of statistical tests of exemplar discriminability and assess the validity (specificity) and power (sensitivity) of each test. The tests include previously used and novel, parametric and nonparametric tests, which treat subject as a random or fixed effect, and are based on different dissimilarity measures, different test statistics, and different inference procedures. We use simulated and real data to determine which tests are valid and which are most sensitive. A popular test statistic reflecting exemplar information is the exemplar discriminability index (EDI), which is defined as the average of the pattern dissimilarity estimates between different exemplars minus the average of the pattern dissimilarity estimates between repetitions of identical exemplars. The popular across-subject t test of the EDI (typically using correlation distance as the pattern dissimilarity measure) requires the assumption that the EDI is 0-mean normal under H0. Although this assumption is not strictly true, our simulations suggest that the test controls the false-positives rate at the nominal level, and is thus valid, in practice. However, test statistics based on average Mahalanobis distances or average linear-discriminant t values (both accounting for the multivariate error covariance among responses) are substantially more powerful for both random- and fixed-effects inference.
... Non-parametric statistical analyses (SnPM) of sLORETA images were performed for each contrast with built-in voxel-wise randomization tests (5000 permutations) and using a log-F-ratio statistic for dependent groups with thresholds of p < 0.01 and p < 0.05, corrected for multiple comparisons. Correction for multiple comparisons in SnPM with random permutations (5000 in the current study) has been shown to yield results similar to those obtained from statistical parametric mapping using a general linear model with multiple comparison corrections based on random field theory [22,23]. ...
... The technique is not limited to a specific number or location of electrodes. Because the sLORETA self-corrects in multiple testing procedures involving all electrodes and/or voxels, and all time samples and/or discrete frequencies via random permutations (5000) in the current study, no further correction is required for multiple comparison [12,22]. The sLORETA has proved to be an efficient tool for functional mapping because it is consistent with physiology and enables correct localization [12]. ...
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Background It is important to assess the degree of brain injury and predict long-term outcomes in neonates diagnosed with hypoxic-ischemic encephalopathy (HIE). However, routine studies, including magnetic resonance imaging (MRI) and conventional encephalography (EEG) or amplitude-integrated EEG (aEEG), have their own limitations in terms of availability and accuracy of evaluation. Recently, quantitative EEG (qEEG) has been shown to improve the predictive reliability of neonatal HIE and has been further refined with brain mapping techniques. Methods We investigated background EEG activities in 29 neonates with HIE who experienced therapeutic hypothermia, via qEEG using a distributed source model. MRI images were evaluated and classified into two groups (normal-to-mild injury vs moderate-to-severe injury), based on a scoring system. Non-parametric statistical analysis using standardized low-resolution brain electromagnetic tomography was performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between the two groups. Results Electrical neuronal activities were significantly lower in the moderate-to-severe injury group compared with the normal-to-mild injury group. Background EEG activities in moderate-to-severe HIE were most significantly reduced in the temporal and parietal lobes. Quantitative EEG also revealed a decrease in background activity at all frequency bands, with a maximum in decrease in the delta component. The maximum difference in current density was found in the inferior parietal lobule of the right parietal lobe for the delta frequency band. Conclusions Our study demonstrated quantitative and topographical changes in EEG in moderate-to-severe neonatal HIE. They also suggest possible implementation and evaluation of conventional EEG and aEEG in neonatal HIE. The findings have implications as biomarkers in the assessment of neonatal HIE.
... Between-group differences of Fisher's z-transformed ISC values (z-scores) were studied using a nonparametric cluster-based two-sample t-test where the statistical significance was determined based on the distribution of 5000 random permutations of z-values between the groups [54][55][56][57][58]. The null distribution was created from the maximum cluster sizes obtained by thresholding the statistical images at the cluster-defining threshold of p < 0.001 at each permutation. ...
... The pairwise cosine similarities within the Finnish-background participants were compared to the pairwise cosine similarities between Russian-background participants using a nonparametric two-tailed t-test with 50,000 permutations [54,[56][57][58]. The t-tests were performed separately for each of the 101 segments. ...
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Perception of the same narrative can vary between individuals depending on a listener’s previous experiences. We studied whether and how cultural family background may shape the processing of an audiobook in the human brain. During functional magnetic resonance imaging (fMRI), 48 healthy volunteers from two different cultural family backgrounds listened to an audiobook depicting the intercultural social life of young adults with the respective cultural backgrounds. Shared cultural family background increased inter-subject correlation of hemodynamic activity in the left-hemispheric Heschl’s gyrus, insula, superior temporal gyrus, lingual gyrus and middle temporal gyrus, in the right-hemispheric lateral occipital and posterior cingulate cortices as well as in the bilateral middle temporal gyrus, middle occipital gyrus and precuneus. Thus, cultural family background is reflected in multiple areas of speech processing in the brain and may also modulate visual imagery. After neuroimaging, the participants listened to the narrative again and, after each passage, produced a list of words that had been on their minds when they heard the audiobook during neuroimaging. Cultural family background was reflected as semantic differences in these word lists as quantified by a word2vec-generated semantic model. Our findings may depict enhanced mutual understanding between persons who share similar cultural family backgrounds.
... Cluster-based permutation tests were used to control for multiple comparisons for the PS and FC measures, following [41]. First, clusters of frequencies and electrodes/sources/links with significant effects (p < 0.05, uncorrected) were created. ...
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Background: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. Methods: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). Results: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. Limitations: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. Conclusions: This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.
... All these age-related tests yielded null results. The nonparametric permutation-based testing was the statistical method used to correct the family-wise error (FWE) [60,61] and the corrected FWE P value (hereinafter P-FWE) was assigned to each fixel after CFE [55] over 5,000 permutations. The outcomes for these two analyses reported in the Results section below were considered statistically significant when per-fixel P-FWE < 0.05. ...
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Background Neuroimage literature of autism spectrum disorder (ASD) has a moderate-to-high risk of bias, partially because those combined with intellectual impairment (II) and/or minimally verbal (MV) status are generally ignored. We aimed to provide more comprehensive insights into white matter alterations of ASD, inclusive of individuals with II (ASD-II-Only) or MV expression (ASD-MV). Methods Sixty-five participants with ASD (ASD-Whole; 16.6 ± 5.9 years; comprising 34 intellectually able youth, ASD-IA, and 31 intellectually impaired youth, ASD-II, including 24 ASD-II-Only plus 7 ASD-MV) and 38 demographic-matched typically developing controls (TDC; 17.3 ± 5.6 years) were scanned in accelerated diffusion-weighted MRI. Fixel-based analysis was undertaken to investigate the categorical differences in fiber density (FD), fiber cross section (FC), and a combined index (FDC), and brain symptom/cognition associations. Results ASD-Whole had reduced FD in the anterior and posterior corpus callosum and left cerebellum Crus I, and smaller FDC in right cerebellum Crus II, compared to TDC. ASD-IA, relative to TDC, had no significant discrepancies, while ASD-II showed almost identical alterations to those from ASD-Whole vs. TDC. ASD-II-Only had greater FD/FDC in the isthmus splenium of callosum than ASD-MV. Autistic severity negatively correlated with FC in right Crus I. Nonverbal full-scale IQ positively correlated with FC/FDC in cerebellum VI. FD/FDC of the right dorsolateral prefrontal cortex showed a diagnosis-by-executive function interaction. Limitations We could not preclude the potential effects of age and sex from the ASD cohort, although statistical tests suggested that these factors were not influential. Our results could be confounded by variable psychiatric comorbidities and psychotropic medication uses in our ASD participants recruited from outpatient clinics, which is nevertheless closer to a real-world presentation of ASD. The outcomes related to ASD-MV were considered preliminaries due to the small sample size within this subgroup. Finally, our study design did not include intellectual impairment-only participants without ASD to disentangle the mixture of autistic and intellectual symptoms. Conclusions ASD-associated white matter alterations appear driven by individuals with II and potentially further by MV. Results suggest that changes in the corpus callosum and cerebellum are key for psychopathology and cognition associated with ASD. Our work highlights an essential to include understudied subpopulations on the spectrum in research.
... Cognitive impairment occurred when the NFT expanded to the neocortex. Some studies on autopsy and cerebrospinal fluid examination (Nichols and Holmes, 2002;van Rossum et al., 2012;Kantarci et al., 2017) suggested that the relationship between tau protein and cognition was more obvious than that between Aβ and cognition. The advents of molecular imaging agents that provide quantitative measures of Aβ and tau have allowed researchers to explore these proteins in AD in vivo. ...
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Objective This study characterizes glucose metabolism and tau protein deposition distribution in patients with Alzheimer’s disease (AD) and to evaluate the relationships between neuropsychological performance and tau protein deposition or glucose metabolism using ¹⁸ F-FDG and ¹⁸ F-AV1451 positron emission tomography/computed tomography (PET/CT). Methods Sixty-four patients with β-amyloid-positive (Aβ+) AD and twenty-five healthy participants were enrolled in this study. All participants underwent ¹⁸ F-FDG and ¹⁸ F-AV1451 PET/CT. Clinical data and neuropsychological scores were collected. Patients with AD were divided into mild, moderate, and severe groups according to mini-mental state examination (MMSE) scores. The standardized uptake value ratios (SUVRs) for both FDG and AV1451 PET images were calculated using the cerebellar vermis as reference. The SUVRs of the whole cerebral cortex and each brain region were calculated. The volume of interest (VOI) was obtained using automated anatomical atlas (AAL) and Brodmann regions. Student’s t -test was used to perform intergroup comparisons of SUVR. The partial correlation coefficient between SUVR and neuropsychological scores was computed in an inter-subject manner using age and education as covariates. Results The mild subgroup showed a reduction in glucose metabolism and aggregation of tau protein in the temporoparietal cortex. With a decline in neuropsychiatric performance, the SUVR on FDG PET decreased and SUVR on tau PET increased gradually. The areas of glucose metabolism reduction and tau protein deposition appeared first in the parietal cortex, followed by the temporal and frontal cortex, successively. Both FDG and tau SUVRs significantly correlated with MMSE, Montreal cognitive assessment (MOCA), auditory verbal learning test (AVLT), Boston naming test (BNT), clock drawing task (CDT), and verbal fluency test (VFT) ( p < 0.05). The SUVR on FDG PET significantly correlated with activities of daily living (ADL) and the Hamilton depression scale (HAMD). There was no significant correlation between the tau SUVRs and ADL or HAMD. Conclusion The extension of tau protein deposition was similar but not exactly consistent with the area of glucose metabolism reduction. Both tau and FDG SUVRs correlated with cognitive function in domain-specific patterns, and the results of FDG PET more closely correlated with neuropsychological function than tau PET results did.
... 39 Mean relative head motion and imaging site were used as covariates in FSL statistical analysis. T-statistic maps with corrected p-values (p < 0.05) were created to evaluate significant differences in the calculated mean maps between the groups, 40 and differences between the PWE and DN groups were analyzed similarly. ...
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Respiratory brain pulsations have recently been shown to drive electrophysiological brain activity in patients with epilepsy. Furthermore, functional neuroimaging indicates that respiratory brain pulsations have increased variability and amplitude in patients with epilepsy compared to healthy individuals. To determine whether the respiratory drive is altered in epilepsy, we compared respiratory brain pulsation synchronicity between healthy controls and patients. Whole brain fast functional magnetic resonance imaging was performed on 40 medicated patients with focal epilepsy, 20 drug-naïve patients and 102 healthy controls. Cerebrospinal fluid associated respiratory pulsations were used to generate individual whole brain respiratory synchronization maps, which were compared between groups. Finally, we analyzed the seizure frequency effect and diagnostic accuracy of the respiratory synchronization defect in epilepsy. Respiratory brain pulsations related to the verified fourth ventricle pulsations were significantly more synchronous in patients in frontal, periventricular and mid-temporal regions, while the seizure frequency correlated positively with synchronicity. The respiratory brain synchronicity had a good diagnostic accuracy (ROC AUC = 0.75) in discriminating controls from medicated patients. The elevated respiratory brain synchronicity in focal epilepsy suggests altered physiological effect of cerebrospinal fluid pulsations possibly linked to regional brain water dynamics involved with interictal brain physiology.
... The number of patients suffering from Alzheimer's 35 disease (AD) increases with an aging population [1]. 36 Unfortunately, there is currently no disease modify-37 ing treatment available [2], causing the doctors and 38 patients to solely rely on symptomatic treatment. 39 Patients with AD have an accumulation of 40 amyloid-␤ (A␤) and tau protein in the neural tissue. ...
Article
Background: Exposure to 40 Hz stroboscopic light, for one hour a day, has previously been published as a potential treatment option for Alzheimer's disease in animal models. However, exposure for an hour a day to 40 Hz stroboscopic light can be strenuous and examining other types of 40 Hz inducing stimuli is paramount if chronic treatment is wanted. Objective: A core assumption behind ensuring a therapeutic outcome is that the visual stimuli can induce 40 Hz gamma entrainment. Here, we examine whether a specific visual stimulus, 40 Hz invisible spectral flicker (ISF), can induce gamma entrainment and how it differs from both continuous light (CON) and 40 Hz stroboscopic light (STROBE). Methods: The study included non-simultaneous EEG-fMRI neuroimaging of 13 young healthy volunteers during light exposure. Each light condition (i.e., CON, ISF, or STROBE) was active for 30 seconds followed immediately by the next. Results: Entrainment of 40 Hz neural activity were significantly higher signal-to-noise ratio during exposure to ISF (mean: 3.03, 95% CI 2.07 to 3.99) and STROBE (mean: 12.04, 95% CI 10.18 to 13.87) compared to CON. Additionally STROBE had a higher entrainment than ISF (mean: 9.01, 95% CI 7.16 to 12.14). Conclusion: This study presents a novel method of 40 Hz entrainment using ISF. This enables the possibility of future randomized placebo-controlled clinical trials with acceptable double blinding due to the essentially imperceivable flicker, which is expected to substantially reduce discomfort compared to interventions with stroboscopic flicker.
... These maps were then voxel-wise compared to test group differences using FSL randomise nonparametric permutation-testing tool. We implemented 5000 permutations using a threshold-free cluster enhance (TFCE) technique to control for multiple comparisons (Nichols & Holmes, 2002). Between groups differences were calculated using a p < 0.05 threshold with voxel-wise changes in the FSL randomise tool. ...
Article
Emotion regulation (ER) is a core element for individual well-being, and dysregulated emotional states are prominent in several mental disorders. Moreover, dispositional use of adaptive ER strategies, such as cognitive reappraisal, is usually associated to better psychological outcomes and less emotional problems. Thus, identifying markers of emotion dysregulation could serve as a key point for developing treatments against risks of psychopathological outcomes. Neuroimaging techniques could represent a useful tool within these aims, focusing on neurobiological markers of psychopathological illness. Given the well known gender differences in using ER strategies, we examined behavioral and neuroimaging patterns associated with dispositional use of reappraisal among a non-clinical female sample. We found that the individual predisposition to use cognitive reappraisal as an emotion regulation strategy was associated with decreased levels of dysregulation. From a neurobiological perspective, difficulties in using reappraisal were associated with decreased resting-state functional connectivity (rs-FC) between the Middle Temporal Gyrus and occipito-parietal regions. Moreover, rs-FC between prefrontal and occipito-parietal brain regions was negatively associated with emotion dysregulation levels. Microstructural anomalies across white matter tracts connecting temporal, parietal, and occipital brain regions were associated to difficulties in using reappraisal. Our findings suggest that specific behavioral and neurobiological substrates are linked to reappraising abilities. Furthermore, the ability to implement adaptive ER strategies could serve as protective factor against developing emotion dysregulation.
... Each participant's behavioral RDM was correlated to the feature RDMs, and the resulting Kendall's τ A values were tested against chance using one-tailed sign permutation testing (5000 iterations). P-values were omnibus-corrected for multiple comparisons using a maximum correlation threshold across all models (Nichols and Holmes, 2002). ...
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Humans observe actions performed by others in many different visual and social settings. What features do we extract and attend when we view such complex scenes, and how are they processed in the brain? To answer these questions, we curated two large-scale sets of naturalistic videos of everyday actions and estimated their perceived similarity in two behavioral experiments. We normed and quantified a large range of visual, action-related and social-affective features across the stimulus sets. Using a cross-validated variance partitioning analysis, we found that social-affective features predicted similarity judgments better than, and independently of, visual and action features in both behavioral experiments. Next, we conducted an electroencephalography (EEG) experiment, which revealed a sustained correlation between neural responses to videos and their behavioral similarity. Visual, action, and social-affective features predicted neural patterns at early, intermediate and late stages respectively during this behaviorally relevant time window. Together, these findings show that social-affective features are important for perceiving naturalistic actions, and are extracted at the final stage of a temporal gradient in the brain.
... Given the poor inter-rater reliability of visual assessment of intervention effects through visual analysis, the SCED package also permits quantitative analysis of data [71,72]. As such the package calculates p values via robust, non-parametric permutation tests and calculates three robust effect sizes (for a primer on commonly reported effect sizes in SCED research see Parker et al.): median difference between conditions, Ruscio's A (also known as the Common Language Effect Size, the Probability of Superiority, and Nonoverlap All Pairs), and Hedge's g [73][74][75][76][77][78][79]. ...
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Psychological intervention targeting distress is now considered an integral component of inflammatory bowel disease (IBD) management. However, significant barriers to access exist which necessitate the development of effective, economic, and accessible brief and remote interventions. Acceptance and commitment therapy (ACT) is a therapy with demonstrated acceptability and a growing evidence base for the treatment of distress in IBD populations. The present paper trialled two brief ACT interventions via randomized multiple baseline designs. Study 1 trialled a single-session ACT intervention (delivered face-to-face and lasting approximately two hours) targeting stress and experiential avoidance, respectively. Participants were seven people with an IBD diagnosis who presented with moderate to extremely severe stress (five females, two males; M age = 39.57, SD = 5.74). The findings of study 1 indicate that a single-session ACT intervention represented an insufficient dosage to reduce stress and experiential avoidance. Study 2 investigated a brief telehealth ACT intervention (delivered via a video conferencing platform and lasting approximately four hours) targeting stress and increased psychological flexibility. Participants (N = 12 people with an IBD diagnosis and mild to extremely severe stress) completed baselines lasting from 21 to 66 days before receiving a two-session ACT telehealth intervention supplemented by a workbook and phone consultation. Approximately half of participants experienced reduced stress, increased engagement in valued action, and increased functioning. Despite shortcomings such as missing data and the context of COVID-19, the present findings suggest that brief ACT interventions in this population may be effective and economic, though further research and replications are necessary.
... The whole-brain voxel-by-voxel one-simple t test (corrected for multiple testing; Nichols & Holmes, 2002) showed that the peak voxel of state 3 in the HAF condition was located in the Brodmann area 9: ...
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Facial attractiveness judgment largely depends on the characteristics of the facial structure and the personality of the observer. However, little is known about the influence of contextual variations on facial attractiveness. In this electroencephalogram study, participants judged the attractiveness of faces presented individually or in pairs with either a higher‐attractive face (HAF) or lower‐attractive face (LAF). The attractiveness judgment rating of the target face was significantly higher when presented in pairs with HAFs or LAFs than when presented individually and was accompanied by a larger late positive complex. These results suggest that contextual faces enhance the attractiveness judgment of target faces. Microstate analyses revealed that the global field power (GFP) of state 3 was significantly correlated with the attractiveness judgment in the HAF condition whereas the GFP of state 2 was significantly correlated with the attractiveness judgment in the LAF condition. Interestingly, the GFP of state 2 mediated the relationship between narcissism and facial attractiveness judgment in the context of LAFs. Source location analyses showed that states 3 and 2 activated the superior and middle frontal gyrus, which are involved in emotion processing. Our findings suggest that facial attractiveness can be enhanced by contextual comparison with other faces, subject to personality of the observer. By using the electroencephalogram (EEG), the spatiotemporal dynamics of the brain were measured when faces were paired with high and low attractiveness faces. Our results show that the face was judged more attractive than it presents alone when it appears with others faces. Our findings contribute to the understanding of neurocognitive mechanisms and processes of facial attractiveness.
... As recommended for whole-brain FBA, Left Lesioned Patients (LLP) and Right Lesioned Patients (RLP) were compared against HC in separate analyses (Dhollander et al., 2021a). Based on the framework of connectivity-based fixel enhancement (Raffelt et al., 2015), statistical inference was performed for each fixel using a general linear model and non-parametric permutation testing over 5000 permutations (Nichols and Holmes, 2001). Nuisance variables included sex for FD, and sex and intracranial volume for FC and FDC comparisons. ...
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Introduction Neonatal arterial ischemic stroke (NAIS) has been shown to affect white matter (WM) microstructure beyond the lesion. Here, we employed fixel-based analysis, a technique which allows to model and interpret WM alterations in complex arrangements such as crossing fibers, to further characterize the long-term effects of NAIS on the entire WM outside the primary infarct area. Materials and Methods 32 children (mean age 7.3 years (SD 0.4), 19 male) with middle cerebral artery NAIS (18 left hemisphere, 14 right hemisphere) and 31 healthy controls (mean age 7.7 years (SD 0.6), 16 male) underwent diffusion MRI scans and clinical examination for manual dexterity. Microstructural and macrostructural properties of the WM were investigated in a fixel-based whole-brain analysis, which allows to detect fiber-specific effects. Additionally, tract-averaged fixel metrics in interhemispheric tracts, and their correlation with manual dexterity, were examined. Results Significantly reduced microstructural properties were identified, located within the parietal and temporal WM of the affected hemisphere, as well as within their interhemispheric connecting tracts. Tract-averaged fixel metrics showed moderate, significant correlation with manual dexterity of the affected hand. No increased fixel metrics or contralesional alterations were observed. Discussion Our results show that NAIS leads to long-term alterations in WM microstructure distant from the lesion site, both within the parietal and temporal lobes as well as in their interhemispheric connections. The functional significance of these findings is demonstrated by the correlations with manual dexterity. The localization of alterations in structures highly connected to the lesioned areas shift our perception of NAIS from a focal towards a developmental network injury.
... To explore group differences at the anatomical level, we analyzed the intersected SC matrix with all the links above-threshold common to both meditators and controls, as described in the previous section ( Fig. 2) using the "Network-Based Statistic Toolbox v1.2 (NBS)" (Zalesky et al. 2010). The NBS, which has been designed to test the hypothesis under the connectome framework, is the network-based equivalent of the suprathreshold cluster-based test (Bullmore et al. 1999;Nichols and Holmes 2002). ...
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In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain’s structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals’ spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.
... Group-wise differences of the Jacobian in the basal ganglia structures between the LID group and non-LID groups were assessed with non-parametric permutation tests adjusted for age, disease duration, treatment duration for levodopa medication, LED for levodopa and COMT inhibitor, and dopamine agonist, and UPDRS part 3 sub-scores (Nichols and Holmes, 2001). We performed the permutation tests by randomly assigning LID and non-LID groups 10,000 times. ...
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Background Despite the clinical impact of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD), the mechanism, especially the role of basal ganglia (BG), is not fully elucidated yet. We investigated the BG structural changes related to LID in PD using a surface-based shape analysis technique. Methods We recruited patients with PD who developed LID within 3 years (LID group, 28 patients) and who did not develop it after 7 years (non-LID group, 35 patients) from levodopa treatment for the extreme case-control study. BG structure volumes were measured using volumetry analysis and the surface-based morphometry feature (i.e., Jacobian) from the subcortical surface vertices. We compared the volume and Jacobian of meshes in the regions between the two groups. We also performed a correlation analysis between local atrophy and the severity of LID. Additionally, we evaluated structural connectivity profiles from globus pallidus interna and externa (GPi and GPe) to other brain structures based on the group comparison. Results The demographic and clinical data showed no significant difference except for disease duration, treatment duration, parkinsonism severity, and levodopa equivalent dose. The LID group had more local atrophies of vertices in the right GPi than the non-LID group, despite no difference in volumes. Furthermore, the LID group demonstrated significantly reduced structural connectivity between left GPi and thalamus. Conclusion This is the first demonstration of distinct shape alterations of basal ganglia structures, especially GPi, related to LID in PD. Considering both direct and indirect BG pathways share the connection between GPi and thalamus, the BG pathway plays a crucial role in the development of LID.
... To explore any training-induced changes in the EEG recordings, pretest and posttest sLORETA images were compared using a paired t-test. Statistical non-parametric mapping of the sLORETA images were performed with sLORETA's built-in voxel-wise randomization tests (5,000 permutations) and by using a log-F-ratio statistic with a threshold of p < 0.05, corrected for multiple comparisons (Nichols & Holmes, 2002). ...
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The physiological function of the Mu rhythm (8–13 Hz in the central region) is still unclear, particularly its role in visuomotor performance in sports (shooting vs. golf putting), as both the complexity of the motor skills (i.e., simple vs. complex visuomotor skills) and the skill level (e.g., novices vs. experts or low-skilled vs. highly skilled) may modulate Mu rhythm. To gain a broader understanding of the association between Mu rhythm and visuomotor skill performance, a study design that considers both a control moderator (the difference in skill level) and the ability to manipulate Mu rhythm (i.e., either increase or decrease Mu rhythm) is required. To achieve this, we recruited 30 novice golfers who were randomly assigned to either the increased Mu rhythm group (IMG), decreased Mu rhythm group (DMG), or sham group (SG) and used electroencephalographic-neurofeedback training (EEG-NFT) to manipulate Mu rhythm during a golf putting task (complex visuomotor skill). The aim was to determine whether the complexity of the motor skill was a potential moderator of Mu rhythm. We mainly found that Mu power was significantly decreased in the DMG following EEG-NFT, which lead to increased motor control and improved performance. We suggest that (1) the complexity of the motor skill, rather than the difference in skill level, may be a potential moderator of Mu rhythm and visuomotor performance, as our results were not consistent with a previous study that reported that increased Mu rhythm improved shooting performance (a simple visuomotor task) in novices.
... A formal framework for testing between two groups in topological data analysis is presented in [45], with an extension to three groups in [14]. Practical examples on nonparametric permutation tests at an acceptable level can be found in [39]. Refer to [43] and [19] for more examples regarding permutation and randomization tests in functional brain imaging and connectivity. ...
Preprint
Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach which can deal with various data modalities of varying complexities. One of the most commonly used tools in TDA is persistent homology (PH) which can extract topological properties from data at various scales. Our aim in this article is to introduce TDA concepts to a statistical audience and provide an approach to analyze multivariate time series data. The application focus will be on multivariate brain signals and brain connectivity networks. Finally, the paper concludes with an overview of some open problems and potential application of TDA to modeling directionality in a brain network as well as the casting of TDA in the context of mixed effects models to capture variations in the topological properties of data collected from multiple subjects
... Test set accuracies (mean±std) for a leave-one-session-out CV scheme (26 sessions). Two-sided, paired, permutation (1e4 permutations) t-tests were computed to identify significant differences between SPDBN(m+v) and the other SPD/TS BN methods [34]. P values were corrected for multiple comparisons with the t-max method. ...
Conference Paper
Symmetric positive definite (SPD) matrices, and in particular covariance matrices as data descriptors find widespread application in various fields but also pure machine learning. SPD matrices form a Riemannian manifold, demanding machine learning methods that take this structure into account. In this work, we extend upon previous works and propose a batch normalization algorithm for the SPD manifold that can be readily combined with SPD neural networks and unlike previous works controls both the Fr´echet mean and variance on the SPD manifold. The proposed method is validated in simulations and datasets with small sample sizes from three application domains: action recognition from human motion trajectories, image classification and mental imagery detection of electroencephalographic (EEG) signals. The combined results show a systematic performance increase upon previous works and tangent space approximations, as well as improved robustness to low signal-to-noise ratios and lack of data as often encountered in neuroimaging and brain-computer interfacing (BCI).
... Permutation tests are non-parametric statistical tests that rely on the randomization of the observed data to assess the statistical significance of group differences (Nichols and Holmes, 2001). In our case, we have collected two sets of functional connectivity matrices, one with N + patients with ADHD { + i | i = 1, ..., N + } and the other one with N − healthy controls { − i | i = 1, ..., N − }. ...
Article
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The use of multi-site datasets in neuroimaging provides neuroscientists with more statistical power to perform their analyses. However, it has been shown that the imaging-site introduces variability in the data that cannot be attributed to biological sources. In this work, we show that functional connectivity matrices derived from resting-state multi-site data contain a significant imaging-site bias. To this aim, we exploited the fact that functional connectivity matrices belong to the manifold of symmetric positive-definite (SPD) matrices, making it possible to operate on them with Riemannian geometry. We hereby propose a geometry-aware harmonization approach, Rigid Log-Euclidean Translation, that accounts for this site bias. Moreover, we adapted other Riemannian-geometric methods designed for other domain adaptation tasks and compared them to our proposal. Based on our results, Rigid Log-Euclidean Translation of multi-site functional connectivity matrices seems to be among the studied methods the most suitable in a clinical setting. This represents an advance with respect to previous functional connectivity data harmonization approaches, which do not respect the geometric constraints imposed by the underlying structure of the manifold. In particular, when applying our proposed method to data from the ADHD-200 dataset, a multi-site dataset built for the study of attention-deficit/hyperactivity disorder, we obtained results that display a remarkable correlation with established pathophysiological findings and, therefore, represent a substantial improvement when compared to the non-harmonization analysis. Thus, we present evidence supporting that harmonization should be extended to other functional neuroimaging datasets and provide a simple geometric method to address it.
... On the other hand, the nonparametric fMRI analysis offers the freedom to use a test statistic to compare experimental conditions. This robustly corrects for multiple comparisons and allows the incorporation of biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test [43,45]. ...
Article
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Inhibitory impairments may persist after abstinence in individuals with alcohol use disorder (AUD). Using traditional statistical parametric mapping (SPM) fMRI analysis, which requires data to satisfy parametric assumptions often difficult to satisfy in biophysical system as brain, studies have reported equivocal findings on brain areas responsible for response inhibition, and activation abnormalities during inhibition found in AUD persist after abstinence. Research is warranted using newer analysis approaches. fMRI scans were acquired during a Go/NoGo task from 30 abstinent male AUD and 30 healthy control participants with the objectives being (1) to characterize neuronal substrates associated with response inhibition using a rigorous nonparametric permutation-based fMRI analysis and (2) to determine whether these regions were differentially activated between abstinent AUD and control participants. A blood oxygen level dependent contrast analysis showed significant activation in several right cortical regions and deactivation in some left cortical regions during successful inhibition. The largest source of variance in activation level was due to group differences. The findings provide evidence of cortical substrates employed during response inhibition. The largest variance was explained by lower activation in inhibition as well as ventral attentional cortical networks in abstinent individuals with AUD, which were not found to be associated with length of abstinence, age, or impulsiveness.
... We tested for differences in EEG activity between groups of participants using cluster-based permutation tests. 20 Briefly, we performed Wilcoxon rank-sum tests as a first-level statistic. We considered a cluster to be the contiguous extend of tests that exceeded a significance threshold in the frequency (n = 47)×electrode (n = 20) space with a total of 940 comparisons. ...
Article
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It is unknown whether alterations in EEG brain activity caused by Huntington's disease may be responsive to huntingtin-lowering treatment. We analysed EEG recordings of 46 patients (mean age = 47.02 years; standard deviation = 10.19 years; 18 female) with early-manifest Stage 1 Huntington's disease receiving the huntingtin-lowering antisense oligonucleotide tominersen for 4 months or receiving placebo as well as 39 healthy volunteers (mean age = 44.48 years; standard deviation = 12.94; 22 female) not receiving treatment. Patients on tominersen showed increased resting-state activity within a 4-8 Hz frequency range compared with patients receiving placebo (cluster-based permutation test, P , 0.05). The responsive frequency range overlapped with EEG activity that was strongly reduced in Huntington's disease compared with healthy controls (cluster-based permutation test, P , 0.05). The underlying mechanisms of the observed treatment-related increase are unknown and may reflect neural plasticity as a consequence of the molecular pathways impacted by tominersen treatment. Hawellek et al. report that patients with Huntington's disease treated with the huntingtin-lowering antisense oligonucleotide to-minersen exhibited increased EEG power in the theta/alpha frequency range. The underlying mechanisms of the observed changes are unknown and may reflect neural plasticity as a consequence of the molecular pathways impacted by tominersen treatment.
... The Montreal Neurologic Institute average MRI brain (MNI152) [50] was used as a realistic head model where the solution space was restricted to the cortical grey matter, corresponding to 6239 voxels at 5 × 5 × 5 mm spatial resolution. Statistical nonparametric mapping (SnMP) with 5000 permutations was used to determine the significant threshold value for voxel activation [51]. ...
Article
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The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. However, the relationship between the ongoing experience of mind wandering and the individual biological signal is still unclear. We aim to estimate associations between subjective experiences measured with the Amsterdam Resting-State Questionnaire and data-driven components of an electroencephalogram extracted by frequency principal component analysis (f-PCA). Five minutes of resting multichannel EEG was recorded in 226 participants and six EEG data-driven components were extracted—three components in the alpha range (peaking at 9, 10.5, and 11.5 Hz) and one each in the delta (peaking at 0.5 Hz), theta (peaking at 5.5 Hz) and beta (peaking at 17 Hz) ranges. Bayesian Pearson’s correlation revealed a positive association between the individual loadings of the theta component and ratings for Sleepiness (r = 0.200, BF10 = 7.676), while the individual loadings on one of the alpha components correlated positively with scores for Comfort (r = 0.198, BF10 = 7.115). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.
... Executing this command resulted in projecting all individual FA images onto the mean FA skeleton and generating a four-dimensional image file containing the skeletonized FA data for all participants. Finally, using a binary mask of the FA skeletonized image as a mask image, a voxel-wise statistical comparison between the intervention and control groups was performed on the four-dimensional skeleton image file using FSL's randomise program (Winkler et al., 2014) with the number of random permutations set to 5,000 (Nichols and Holmes, 2002). Data for the other DTI metrics, including MD, AD, and RD, were analyzed using the tbss_non_FA script to generate a four-dimensional skeleton image file for each of the other DTI metrics. ...
Article
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Diffusion tensor imaging (DTI) enables the investigation of white matter properties in vivo by applying a tensor model to the diffusion of water molecules in the brain. Using DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), an attempt has been made to detect age-related alterations in the white matter microstructure in aging research. However, the use of comprehensive DTI measures to examine the effects of cognitive intervention/training on white matter fiber health in older adults remains limited. Recently, we developed a cognitive intervention program called Photo-Integrated Conversation Moderated by Robots (PICMOR), which utilizes one of the most intellectual activities of daily life, conversations. To examine the effects of PICMOR on cognitive function in older adults, we conducted a randomized controlled trial and found that verbal fluency task scores were improved by this intervention. Based on these behavioral findings, we collected in this pilot study diffusion-weighted images from the participants to identify candidate structures for white matter microstructural changes induced by this intervention. The results from tract-based spatial statistics analyses showed that the intervention group, who participated in PICMOR-based conversations, had significantly higher FA values or lower MD, AD, or RD values across various fiber tracts, including the left anterior corona radiata, external capsule, and anterior limb of the internal capsule, compared to the control group, who participated in unstructured free conversations. Furthermore, a larger improvement in verbal fluency task scores throughout the intervention was associated with smaller AD values in clusters, including the left side of these frontal regions. The present findings suggest that left frontal white matter structures are candidates for the neural underpinnings responsible for the enhancement of verbal fluency. Although our findings are limited by the lack of comparable data at baseline, we successfully confirmed the hypothesized pattern of group differences in DTI indices after the intervention, which fits well with the results of other cognitive intervention studies. To confirm whether this pattern reflects intervention-induced white matter alterations, longitudinal data acquisition is needed in future research.
... We assessed the statistical significance across participants with statistical non-parametric mapping (Nichols and Holmes, 2002) using the SnPM13 software, using FWE-correction at the voxel level to control for multiple comparisons (http://warwick. ac.uk/snpm). ...
Article
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Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate pattern dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for multivariate pattern dependence. The toolbox includes linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.
... A statistical parametric mapping (SPM) technique was used to assess the time series parameters such as one-dimensional (1D) kinematic and force trajectories (Pataky et al., 2015;Besson et al., 2019). SPM paired t-tests were performed on shoe effects for every 1D parameter (Nichols and Holmes, 2002). SPM tests were calculated with SPM1D v0.4 for MATLAB (www.spm1d.org, ...
Article
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The study aimed to research the effects of innovative running shoes (a high heel-to-toe drop and special structure of midsole) on the biomechanics of the lower limbs and perceptual sensitivity in female runners. Fifteen healthy female runners were recruited to run through a 145-m runway with planted force plates at one peculiar speed (3.6 m/s ± 5%) with two kinds of shoe conditions (innovative running shoes vs. normal running shoes) while getting biomechanical data. The perception of shoe characteristics was assessed simultaneously through a 15-cm visual analog scale. The statistical parametric mapping technique calculated the time-series parameters. Regarding 0D parameters, the ankle dorsiflexion angle of innovative running shoes at touchdown was higher, and the peak dorsiflexion angle, range of motion, peak dorsiflexion velocity, and plantarflexion moment on the metatarsophalangeal joint of innovative running shoes during running were significantly smaller than those of normal running shoes (all p < 0.001). In addition, the braking phase and the time of peak vertical force 1 of innovative running shoes were found to be longer than those of normal running shoes (both p < 0.05). Meanwhile, the average vertical loading rate 1, peak vertical loading rate 1, peak braking force, and peak vertical force 1 in the innovative running shoes were lower than those of the normal running shoes during running (both p < 0.01). The statistical parametric mapping analysis exhibited a higher ankle dorsiflexion angle (0–4%, p < 0.05), a smaller knee internal rotation angle (0–6%, p < 0.05) (63–72%, p < 0.05), a decreased vertical ground reaction force (11–17%, p = 0.009), and braking anteroposterior ground reaction force (22–27%, p = 0.043) for innovative running shoes than normal running shoes. Runners were able to perceive the cushioning of innovative running shoes was better than that of normal running shoes. These findings suggested combining the high offset and structure of the midsole would benefit the industrial utilization of shoe producers in light of reducing the risk of running injuries for female runners.
... Comparisons of scalp power maps were performed separately for each frequency band. At the scalp level, multiple comparison adjustment was performed using a non-parametric cluster-based permutation test, 32 as described in previous works. 31,33,34 Specifically, for each performed test, a null distribution was generated by randomly shuffling the grouplabel of each subject for comparisons. ...
Article
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Purpose: Disorders of arousal (DoA) are characterized by incomplete awakening from NREM sleep, with the admixture of both deep sleep and wake EEG activity. Previous observations suggested that changes in EEG activity could be detected in the seconds preceding DoA episodes. The aims of this work were to characterize the topography of EEG spectral changes prior to DoA episodes and to investigate whether or not behavioral complexity could be predicted by changes in EEG immediately preceding behavioral onsets. Patients and methods: We collected 103 consecutive video-polysomnographic recordings of 53 DoA adult patients and classified all episodes as simple, rising and complex arousal movements. For each episode, a 5-second window preceding its motor onset ("pre-event") and a 60-second window from 2 to 3 minutes before the episodes ("baseline") were compared. Subsequently, a between-group comparison was performed for the pre-event of simpler versus the more complex episodes. Results: Spectral analysis over 325 DoA episodes showed an absolute significant increase prior to DoA episodes in all frequency bands excluding sigma, which displayed the opposite effect. In normalized maps, the increase was relatively higher over the central/anterior areas for both slow and fast frequency bands. No significant differences emerged from the comparison between simpler and more complex episodes. Conclusion: Taken together, these results show that deep sleep and wake-like EEG rhythms coexist over overlapping areas before DoA episodes, suggesting an alteration of local sleep mechanisms. Episodes of different complexity are preceded by a similar EEG activation, implying that they possibly share a similar pathophysiology.
... However, the brain regions of interest are located close to each other within the prefrontal cortex and hemodynamically highly correlated with each other (i.e., independent of each other), using classical parametric statistics with standard procedures for multiple comparison correction (e.g., Bonferroni correction) has the potential to inflate false negatives. This non-parametric resamplingbased approach has been suggested as an effective and intuitive option for multiple comparison problems in fNIRS research (33)(34)(35). We ran 1000 permutations in each brain region of interest through R packages (R Foundation for Statistical Computing) and the "lmPerm" package was used for the analyses. ...
Article
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Background Few previous studies have focused on prefrontal activation in young adults diagnosed with major depressive disorder (MDD) and suicidality via functional near-infrared spectroscopy (fNIRS).Materials and MethodsA total of 59 healthy controls (HCs), 35 patients with MDD but without suicidality, and 25 patients with MDD and suicidality, between the ages of 18–34 years, were enrolled. Changes in oxygenated hemoglobin (oxy-Hb) levels of the prefrontal cortex at baseline, 4 weeks, and 8 weeks, were evaluated using a protocol consisting of three consecutively repeated trials of rest, speech, and verbal fluency test (VFT) via fNIRS. MDD was diagnosed and suicidality was evaluated based on Mini International Neuropsychiatric Interview (MINI).ResultsOxy-Hb levels were impaired in patients with MDD compared with HCs (p = 0.018 for left prefrontal cortex; p = 0.021 for right ventromedial prefrontal cortex; p = 0.002 for left frontopolar cortex). Among the three groups including HCs, MDD without suicidality, and MDD with suicidality, prefrontal oxygenation was most decreased in MDD patients with suicidality. A significantly impaired prefrontal oxygenation in the right ventrolateral prefrontal cortex (VLPFC) was detected after adjusting for covariates in MDD patients with suicidality, compared to those without suicidality.Conclusion Impaired prefrontal oxygenation during cognitive execution may serve as a diagnostic biomarker for suicidality in young adult patients with MDD.
... Chance level (50%) was then subtracted from these maps, which were then smoothed with a 6-mm Gaussian smoothing kernel (for the purpose of statistical inference) before being analyzed at group level using a one-sample non-parametric permutation test (Nichols & Holmes, 2002) implemented in the Statistical non-Parametric Mapping toolbox (SnPM13; ...
Thesis
Même si nous percevons l'espace qui nous entoure comme un continuum cartésien, la région de l'espace près du corps où se déroulent les interactions physiques avec l'environnement est une région spéciale, appelée espace péri-personnel (EPP). L’EPP a d'abord été défini sur la base des propriétés de neurones enregistrés chez le singe dans des régions cérébrales prémotrices et pariétales spécifiques. Plus récemment, un réseau homologue putatif a été identifié chez l'homme en utilisant l’IRMf. La représentation de cet espace ne fait pas référence à une région bien délimitée avec des frontières claires mais est au contraire flexible, nous permettant d'adapter notre comportement en fonction du contexte. En particulier, le monde des hommes et des singes est avant tout un monde social. Dans ce monde social, une zone de confort est nécessaire pour réguler la distance entre soi et les autres et ainsi éviter l'inconfort, voire l'anxiété. Cependant, on sait encore peu de choses sur cette dimension sociale de l’EPP comparé à celle liée aux objets. Dans ce contexte, mon travail de thèse visait d'abord à combler le fossé entre les propriétés enregistrées dans les neurones individuels du singe et les activités cérébrales identifiées en neuroimagerie du réseau prémoteur-pariétal humain. Deuxièmement, il visait à apporter un nouvel éclairage sur la dimension sociale de l’EPP, un sujet qui a été largement négligé jusqu'à présent alors qu'il est de la plus haute importance pour tous les animaux. Pour répondre à ces questions, j'ai développé des protocoles utilisant un environnement de réalité virtuelle (RV) permettant une manipulation et un contrôle très précis des informations visuelles à différentes distances de notre corps. Pour réaliser mon premier objectif, j’ai utilisé des procédures expérimentales similaires chez l'homme et le singe afin de comparer l'activité cérébrale en IRMf. À travers deux tâches, où des objets réels ou virtuels étaient présentés à différentes distances (proche et éloignée) du corps, j'ai identifié un réseau prémoteur-pariétal homologue sous-jacent à la représentation de l’EPP chez les deux espèces. Pour réaliser mon deuxième objectif, j'ai utilisé une approche multi-échelle. Plus précisément, mon objectif était de comprendre comment les informations sociales (expressions faciales émotionnelles) dans notre EPP affectent nos capacités de perception, notre état physiologique, et notre activité cérébrale. Au niveau comportemental, mes résultats ont montré que nos capacités de discrimination visuelle étaient améliorées lorsque les visages émotionnels étaient présentés dans l’EPP par rapport à l'espace lointain, même lorsque la taille rétinienne était similaire pour les images proches et lointaines. Cette amélioration des capacités perceptives s'accompagnait d'une augmentation de la fréquence cardiaque lorsque les visages émotionnels étaient proches du corps. Enfin, au niveau neuronal, j'ai identifié un réseau occipito-prémoteur-pariétal avec une activité accrue en présence de visages émotionnels proches par rapport aux visages lointains. Mes résultats montrent également qu'un réseau commun code de manière similaire des stimuli sociaux et non sociaux dans l’EPP. Parallèlement à ce travail réalisé chez des volontaires sains, j'ai également établi un lien direct entre des lésions unilatérales médio-temporales et un déficit dans la régulation appropriée des distances sociales. En résumé, mes résultats démontrent que la présence sociale dans l’EPP facilite nos performances comportementales, augmente notre niveau de vigilance et recrute un réseau neuronal prémoteur-pariétal central quelque soit le type d’information (sociale ou non sociale). Ainsi, un réseau neuronal commun permettrait une réponse rapide, qui pourrait être principalement recruté dans n’importe quelle situation se produisant dans notre EPP, et des régions cérébrales supplémentaires pourraient entrer en jeu afin d’affiner notre comportement en fonction du contexte.
... We performed a non-parametric statistical approach for all EEG data analyses which did not depend on assumptions of the data distributions ( Nichols and Holmes, 2002 ). We carried out permutation-based clustersize inference to test the periods during which the decoding accuracy or correlation showed significant effects. ...
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Humans can detect and recognize faces quickly, but there has been little research on the temporal dynamics of the different dimensional face information that is extracted. The present study aimed to investigate the time course of neural responses to the representation of different dimensional face information, such as age, gender, emotion, and identity. We used support vector machine decoding to obtain representational dissimilarity matrices of event-related potential responses to different faces for each subject over time. In addition, we performed representational similarity analysis with the model representational dissimilarity matrices that contained different dimensional face information. Three significant findings were observed. First, the extraction process of facial emotion occurred before that of facial identity and lasted for a long time, which is specific to the right frontal region. Second, arousal was preferentially extracted before valence during the processing of facial emotional information. Third, different dimensional face information exhibited representational stability during different periods. In conclusion, these findings reveal the precise temporal dynamics of multidimensional information processing in faces and provide powerful support for computational models on emotional face perception.
... The tool uses the SnPM methodology known as Fisher's permutation test. 22 Holmes' non-parametric correction procedure for multiple comparisons 23 is integrated and the statistical analysis does not require any assumption of Gaussianity. 24 We used the "t-statistic on Log transformed data" test, with 5000 randomizations and a variance smoothing parameter of 0. This allowed us to do voxel-wise paired comparisons and to calculate the "Log t-test" thresholds corresponding to statistically significant thresholds P < 0.05 and P < 0.01. ...
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Transcranial direct current stimulation (tDCS) applied to the prefrontal cortex has been frequently used to elicit behavioral changes in patients with schizophrenia. However, the interaction between prefrontal tDCS and electrophysiological changes remains largely uncharted. The present study aimed to investigate cortical electrophysiological changes induced by tDCS in frontal areas by means of repeated electroencephalography (EEG) in patients with schizophrenia. In total, 20 patients with schizophrenia received 13 minutes of anodal tDCS (1 mA) applied to the left dorsolateral prefrontal cortex (DLPFC). Repeated resting EEG was recorded before (once) and following (at five follow‐up time‐bins) tDCS to trace post‐tDCS effects. We used sLORETA for source reconstruction to preserve the localization of brain signals with a low variance and to analyze frequency changes. We observed significant changes after the stimulation in areas highly connected with the stimulated DLPFC areas. The alpha 1 (8.5‐10.0 Hz) activity showed a highly significant, long‐lasting, increase for up to 1 hour after the stimulation in the postcentral gyrus (Brodmann area 2, 3, and 40). Significant yet unstable changes were also seen in the alpha‐2 frequency band precentral at 10 minutes, in the beta‐1 frequency band occipital at 20 minutes, and in the beta‐3 frequency band temporal at 40 minutes. We were able to show that anodal tDCS can induce stable EEG changes in patients with schizophrenia. The results underline the potential of tDCS to induce long‐lasting neurophysiological changes in patients with schizophrenia showing the possibility to induce brain excitability changes in this population.
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Skilled reading requires specialized visual cortical processing of orthographic information and its impairment has been proposed as a potential correlate of compromised reading in dyslexia. However, which stage of orthographic information processing during natural reading is disturbed in dyslexics remains unexplored. Here we addressed this question by simultaneously measuring the eye movements and EEG of dyslexic and control young adults during natural reading. Isolated meaningful sentences were presented at five inter-letter spacing levels spanning the range from minimal to extra-large spacing, and participants were instructed to read the text silently at their own pace. Control participants read faster, performed larger saccades and shorter fixations compared to dyslexics. While reading speed peaked around the default letter spacing, saccade amplitude increased and fixation duration decreased with the increase of letter spacing in both groups. Lateralization of occipito-temporal fixation-related EEG activity (FREA) was found in three consecutive time intervals corresponding to early orthographic processing in control readers. Importantly, the lateralization in the time range of the first negative left occipito-temporal FREA peak was specific for first fixations and exhibited an interaction effect between reading ability and letter spacing. The interaction originated in the significant decrease of FREA lateralization at extra-large compared to default letter spacing in control readers and the lack of lateralization in both letter spacing conditions in the case of dyslexics. These findings suggest that expertise-driven hemispheric functional specialization for early orthographic processing thought to be responsible for letter identity extraction during natural reading is compromised in dyslexia.
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Previous studies have revealed that phonological processing of Chinese characters elicited activation in the left prefrontal cortex, bilateral parietal cortex, and occipitotemporal regions. However, it is controversial what role the left middle frontal gyrus plays in Chinese character reading, and whether the core regions (e.g., the left superior temporal gyrus and supramarginal gyrus) for phonological processing of alphabetic languages are also involved in Chinese character reading. To address these questions, the present study used both univariate and multivariate analysis (i.e., representational similarity analysis, RSA) to explore neural representations of phonological information during Chinese character reading. Participants were scanned while performing a reading aloud task. Univariate activation analysis revealed a widely distributed network for word reading, including the bilateral inferior frontal gyrus, middle frontal gyrus, lateral temporal cortex, and occipitotemporal cortex. More importantly, RSA showed that the left prefrontal (i.e., the left middle frontal gyrus and left inferior frontal gyrus) and bilateral occipitotemporal areas (i.e., the left inferior and middle temporal gyrus and bilateral fusiform gyrus) represented phonological information of Chinese characters. These results confirmed the importance of the left middle frontal gyrus and regions in ventral pathway in representing phonological information of Chinese characters.
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Introduction Chronic low back pain (CLBP) is a common disabling health condition. Current treatments demonstrate modest effects, warranting newer therapies. Brain imaging demonstrates altered electrical activities in cortical areas responsible for pain modulation, emotional and sensory components of pain experience. Treatments targeting to change electrical activities of these key brain regions may produce clinical benefits. This pilot study aims to (1) evaluate feasibility, safety and acceptability of a novel neuromodulation technique, high-definition transcranial infraslow pink noise stimulation (HD-tIPNS), in people with CLBP, (2) explore the trend of effect of HD-tIPNS on pain and function, and (3) derive treatment estimates to support sample size calculation for a fully powered trial should trends of effectiveness be present. Methods and analysis A pilot, triple-blinded randomised two-arm placebo-controlled parallel trial. Participants (n=40) with CLBP will be randomised to either sham stimulation or HD-tIPNS (targeting somatosensory cortex and dorsal and pregenual anterior cingulate cortex). Primary outcomes include feasibility and safety measures, and clinical outcomes of pain (Brief Pain Inventory) and disability (Roland-Morris disability questionnaire). Secondary measures include clinical, psychological, quantitative sensory testing and electroencephalography collected at baseline, immediately postintervention, and at 1-week, 1-month and 3 months postintervention. All data will be analysed descriptively. A nested qualitative study will assess participants perceptions about acceptability of intervention and analysed thematically. Ethics and dissemination Ethical approval has been obtained from Health and Disability Ethics Committee (Ref:20/NTB/67). Findings will be reported to regulatory and funding bodies, presented at conferences, and published in a scientific journal. Trial registration number ACTRN12620000505909p.
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There is limited evidence on the effectiveness of choline alphoscerate for mild cognitive impairment (MCI) in studies using neuropsychological markers. The aim of this study was to evaluate the spectral change at a source level using quantitative electroencephalography (qEEG) as a biomarker for cognitive function after choline alphoscerate administration to patients with MCI. This study used the qEEG data of patients with MCI who visited the Department of Neurology of the Chung-Ang University Hospital between April 2017 and December 2018. Resting-state EEG studies were performed on 33 patients with MCI at baseline and compared with those of the 18 normal controls selected from the community. After baseline qEEG, choline alphoscerate 400 mg was administered twice daily for 2 months to the patients with MCI. Follow-up qEEG was performed in 20 subjects. Baseline qEEG of patients with MCI was compared to qEEG after choline alphoscerate administration. We found that the MCI group exhibited a decreased alpha power compared to that of the control group. Patients with MCI treated with choline alphoscerate exhibited a decrease in the theta and delta power of the parietal and temporal lobe and an increase in the alpha power spectrum of the occipital lobes. We also identified the trend of default mode network enhancement after choline alphoscerate administration. Our results suggest that choline alphoscerate may have a positive effect in patients with MCI and support the usefulness of qEEG for monitoring the therapeutic effect of nootropics.
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Altered white matter microstructure has been reported repeatedly using diffusion tensor imaging (DTI) in HIV-associated neurocognitive disorders. However, the associations between neurocognitive deficits and impaired white matter remains obscure due to frequent physical and psychiatric comorbidities in the patients. Severe immune suppression, reflected by low nadir CD4 T-cell counts, is reported to be associated with the neurocognitive deficits in the patients. In the present study, we examined white matter integrity using DTI and tract-based spatial statistics (TBSS), and neurocognitive functions using a battery of tests, in 15 HIV-infected patients with low nadir CD4, 16 HIV-infected patients with high nadir CD4, and 33 age- and sex-matched healthy controls. As DTI measures, we analyzed fractional anisotropy (FA) and mean diffusivity (MD). In addition, we investigated the correlation between white matter impairments and neurocognitive deficits. Among the three participant groups, the patients with low nadir CD4 showed significantly lower performance in processing speed and motor skills, and had significantly increased MD in widespread regions of white matter in both hemispheres. In the patients with low nadir CD4, there was a significant negative correlation between motor skills and MD in the right motor tracts, as well as in the corpus callosum. In summary, this study may provide white matter correlates of neurocognitive deficits in HIV-infected patients with past severe immune suppression as legacy effects.
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Cyclists are exposed for a long period to continuous vibrations. When a muscle is exposed to vibration, its efficiency decreases, the onset of fatigue occurs sooner, and the comfort of the cyclist is reduced. This study characterised the vastus lateralis (VL) soft tissue vibrations for different input frequencies and different pedalling phases. Ten cyclists were recruited to pedal at 55, 70, 85, and 100 rpm on a vibrating cycle ergometer that induced vibrations at frequencies ranging from 14.4 Hz (55 rpm) to 26.3 Hz (100 rpm). The VL vibration amplitude was quantified with a continuous wavelet transform and expressed as a function of the crank angle. The pedalling cycle was split into four phases (downstroke, backstroke, upstroke, and overstroke) to express the mean vibration amplitude and frequency of each phase. Statistical analysis depicted that VL vibration frequency increased with the pedalling cadence and that the VL was exposed to up to 50% more vibration amplitudes during the downstroke phase at a slow cadence. The increase in the pedal vibration frequency, a higher vibration transmission due to greater normal force on the pedal, and strong activation of the VL during the downstroke phase were discussed to explain these results.
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Apathy is the most common and disabling non-cognitive feature of dementia, affecting up to 90% of individuals over the disease course. Despite its prevalence, the underlying mechanisms of apathy remain elusive. This study aimed to investigate whether cognitive apathy and executive functioning have a shared cognitive and neural basis, in behavioural variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD). Seventy-one participants (31 bvFTD, 17 AD and 23 controls) were assessed on a neuropsychological battery of executive tasks including the Zoo Map Test, Modified Six Elements Test, Tower Test and verbal fluency. The Dimensional Apathy Scale (DAS) was used to quantify cognitive apathy. Principal components analysis identified a single component underpinning performance on the neuropsychological tests, with both bvFTD and AD showing significantly reduced "planning ability” compared to controls. On the DAS, 74% of bvFTD patients and 59% of AD patients showed clinically significant cognitive apathy. Importantly, linear regression revealed that lower planning ability significantly predicted increased cognitive apathy, even after controlling for cognitive impairment and disease duration. Voxel-based morphometry analyses revealed that planning ability and cognitive apathy were both associated with atrophy of the right frontal pole and orbitofrontal cortex, as well as the thalamus and putamen. From a theoretical perspective, our results reveal a shared mechanism underpinning both cognitive apathy and planning deficits in bvFTD and AD. Clinically, this knowledge will help to improve the identification of apathy in clinical syndromes and inform targeted interventions to improve independence and wellbeing for those living with dementia.
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The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites¹ that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.
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How do life experiences impact cortical function? In people who are born blind, the “visual” cortices are recruited during nonvisual tasks, such as Braille reading and sound localization. Do visual cortices have a latent capacity to respond to nonvisual information throughout the lifespan? Alternatively, is there a sensitive period of heightened plasticity that makes visual cortex repurposing especially possible during childhood? To gain insight into these questions, we leveraged meaningful naturalistic auditory stimuli to simultaneously engage a broad range of cognitive domains and quantify cross-modal responses across congenitally blind (n = 22), adult-onset blind (vision loss >18 years-of-age, n = 14) and sighted (n = 22) individuals. During fMRI scanning, participants listened to two types of meaningful naturalistic auditory stimuli: excerpts from movies and a spoken narrative. As controls, participants heard the same narrative with the sentences shuffled and the narrative played backwards (i.e., meaningless sounds). We correlated the voxel-wise timecourses of different participants within condition and group. For all groups, all stimulus conditions induced synchrony in auditory cortex while only the narrative stimuli synchronized responses in higher-cognitive fronto-parietal and temporal regions. As previously reported, inter-subject synchrony in visual cortices was higher in congenitally blind than sighted blindfolded participants and this between-group difference was particularly pronounced for meaningful stimuli (movies and narrative). Critically, visual cortex synchrony was no higher in adult-onset blind than sighted blindfolded participants and did not increase with blindness duration. Sensitive period plasticity enables cross-modal repurposing in visual cortices.
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Reviews the book, Human brain function by R. S. J. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, and J. C. Mazziotta (1997). A significant recent development in the rapidly evolving field of imaging technology has been the relative shift of emphasis from positron emission tomography (PET) activation studies to functional magnetic resonance imaging (MRI). Not only is MRI technology more widely available than PET, it is less invasive and can benefit from improved temporal and spatial resolution. It seems timely, therefore, to take stock and ask what has been learned from human brain imaging so far. This volume, by Frackowiak and colleagues from the Functional Imaging Laboratory in London, seeks to do that through a comprehensive overview of the group's own work in the field to date; that is, mainly through the use of PET. Physiologists, in particular, may find the title somewhat misleading; "Human Brain Function" focuses almost exclusively on the results of PET activation studies, while relevant electrophysiological studies are only given detailed consideration in one chapter on the visual system. In spite of this, the book does demonstrate, unequivocally, that functional neuroimaging has had a dramatic impact on the study of human brain function. What also continues to be clear, however, is that the gulf between technological know how and depth of understanding has never been wider. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Many studies of brain function with positron emission tomography (PET) involve the interpretation of a subtracted PET image, usually the difference between two images under baseline and stimulation conditions. The purpose of these studies is to see which areas of the brain are activated by the stimulation condition. In many cognitive studies, the activation is so slight that the experiment must be repeated on several subjects and the subtracted images are averaged to improve the signal-to-noise ratio. The averaged image is then standardized to have unit variance and then searched for local maxima. The main problem facing investigators is which of these local maxima are statistically significant. We describe a simple method for determining an approximate p value for the global maximum based on the theory of Gaussian random fields. The p value is proportional to the volume searched divided by the product of the full widths at half-maximum of the image reconstruction process or number of resolution elements. Rather than working with local maxima, our method focuses on the Euler characteristic of the set of voxels with a value larger than a given threshold. The Euler characteristic depends only on the topology of the regions of high activation, irrespective of their shape. For large threshold values this is approximately the same as the number of isolated regions of activation above the threshold. We can thus not only determine if any activation has taken place, but we can also estimate how many isolated regions of activation are present.
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Statistical parametric maps (SPMs) are potentially powerful ways of localizing differences in regional cerebral activity. This potential is limited by uncertainties in assessing the significance of these maps. In this report, we describe an approach that may partially resolve this issue. A distinction is made between using SPMs as images of change significance and using them to identify foci of significant change. In the first case, the SPM can be reported nonselectively as a single mathematical object with its omnibus significance. Alternatively, the SPM constitutes a large number of repeated measures over the brain. To reject the null hypothesis, that no change has occurred at a specific location, a threshold adjustment must be made that accounts for the large number of comparisons made. This adjustment is shown to depend on the SPM's smoothness. Smoothness can be determined empirically and be used to calculate a threshold required to identify significant foci. The approach models the SPM as a stationary stochastic process. The theory and applications are illustrated using uniform phantom images and data from a verbal fluency activation study of four normal subjects.
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Hallucinations, perceptions in the absence of external stimuli, are prominent among the core symptoms of schizophrenia. The neural correlates of these brief, involuntary experiences are not well understood, and have not been imaged selectively. We have used new positron emission tomography (PET) methods to study the brain state associated with the occurrence of hallucinations in six schizophrenic patients. Here we present a group study of five patients with classic auditory verbal hallucinations despite medication, demonstrating activations in subcortical nuclei (thalamic, striatal), limbic structures (especially hippocampus), and paralimbic regions (parahippocampal and cingulate gyri, as well as orbitofrontal cortex). We also present a case study of a unique, drug-naive patient with visual as well as auditory verbal hallucinations, demonstrating activations in visual and auditory/linguistic association cortices as part of a distributed cortical-subcortical network. Activity in deep brain structures, identified with group analysis, may generate or modulate hallucinations, and the particular neocortical regions entrained in individual patients may affect their specific perceptual content. The interaction of these distributed neural systems provides a biological basis for the bizarre reports of schizophrenic patients.
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Many neuropsychiatric symptom states are idiosyncratic, involuntary, randomly occurring, subjective, and transient. The brain states associated with these clinically important mental states cannot be imaged directly with existing positron emission tomography (PET) techniques. A new PET method that brings such mental/brain states under experimental control for analysis in single subjects is described. It utilizes a slow bolus H2 15O three-dimensional (3D) regional CBF imaging technique. The analysis focuses upon natural or experimentally induced variance in the temporal distribution of specific neuropsychological events over the course of a study session. For each scan, the amount of radioactivity entering the brain during these events is calculated to derive a score reflecting the contribution of the events to the image. A statistical analysis is then performed to identify those pixels in which the intensity covaries with the scan scores over the subject's scans. This permits the identification of the brain areas associated with the mental state of interest. The method is validated using an auditory sentence-monitoring task. The detection in single subjects of cerebral activations associated with recurrent events as brief as 2 s in duration is demonstrated. This method may be used as a means of imaging ephemeral neurologic or neuropsychiatric symptom states or as an alternative to a subtraction design for activation studies.
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We present a new method for the analysis of individual brain positron emission tomography (PET) activation maps that looks for activated areas of a certain size rather than pixels with maximum values. High signal-to-noise-ratio pixel clusters (HSC) are identified and their sizes are statistically tested with respect to a Monte-Carlo-derived distribution of cluster sizes in pure noise images. From multiple HSC size tests, a strategy is proposed for control of the overall type I error. The sensitivity and specificity of this method have been assessed using realistic Monte Carlo simulations of brain activation maps. When compared with the gamma 2 statistic of the local maxima distribution, the proposed method showed enhanced sensitivity, particularly for signals of low magnitude and/or large size. Its potential for the individual analysis of PET activation studies is presented in two sets of subjects who underwent two cognitive protocols. Although it can be viewed as an alternative to the classical stereotactic averaging approach, this new method is intended to be a first step toward the analysis of single-subject PET activation studies.
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In pursuing our work on the organization of human visual cortex, we wanted to specify more accurately the position of the visual motion area (area V5) in relation to the sulcal and gyral pattern of the cerebral cortex. We also wanted to determine the intersubject variation of area V5 in terms of position and extent of blood flow change in it, in response to the same task. We therefore used positron emission tomography (PET) to determine the foci of relative cerebral blood flow increases produced when subjects viewed a moving checkerboard pattern, compared to viewing the same pattern when it was stationary. We coregistered the PET images from each subject with images of the same brain obtained by magnetic resonance imaging, thus relating the position of V5 in all 24 hemispheres examined to the individual gyral configuration of the same brains. This approach also enabled us to examine the extent to which results obtained by pooling the PET data from a small group of individuals (e.g., six), chosen at random, would be representative of a much larger sample in determining the mean location of V5 after transformation into Talairach coordinates. After stereotaxic transformation of each individual brain, we found that the position of area V5 can vary by as much as 27 mm in the left hemisphere and 18 mm in the right for the pixel with the highest significance for blood flow change. There is also an intersubject variability in blood flow change within it in response to the same visual task. V5 nevertheless bears a consistent relationship, within each brain, to the sulcal pattern of the occipital lobe. It is situated ventrolaterally, just posterior to the meeting point of the ascending limb of the inferior temporal sulcus and the lateral occipital sulcus. In position it corresponds almost precisely with Flechsig's Feld 16, one of the areas that he found to be myelinated at birth.
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We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the P-value for local maxima of Gaussian, t, χ2 and F fields over search regions of any shape or size in any number of dimensions. This unifies the P-values for large search areas in 2-D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699) large search regions in 3-D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) and the usual uncorrected P-value at a single pixel or voxel.
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A simple spatial-correlation model is presented for repeated measures data. Correlation between observations on the same subject is assumed to decay as a linear function of the squared distance separating the regions in three-dimensional space where the observations are made. This quadratic decay (QD) model has many attractive theoretical properties. The covariance structure of the “normalized” data, formed by subtracting the subject average, is the same as that generated by random linear trends plus centered white noise, which leads to a simple method for simulating the original data. It also implies that the first three principal components of the normalized observations are the spatial coordinates of the regions. Thus principal components analysis can be used as an exploratory tool to verify the QD model, provided the white noise component is small. Certain simple predictions can be made, such as the fact that the variances of the normalized observations increase linearly with the squared distance from the centroid of the regions, even though the original observations are assumed to have equal variance. This implies that it is harder to detect abnormal regional measurements in the outlying regions using normalized observations. Assuming multivariate normality, generalized least squares can be used to find maximum likelihood estimates of the QD model; calculations can be reduced using an expression for the inverse of the covariance matrix that only involves the inverse of a 3 × 3 matrix. It is shown, however, that although normalization simplifies the model by removing the random subject effect, it reduces information about the spatial correlation effect, making it harder to detect. This may have implications for other spatial-correlation models that are fitted to residuals from a sample mean. The QD model is applied to 30 cerebral regional glucose metabolism measurements from positron emission tomography (PET) images on a group of 20 normal subjects.
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The distribution of the size of one connected component and the largest connected component of the excursion set is derived for stationary χ2, t and F fields, in the limit of high or low thresholds. This extends previous results for stationary Gaussian fields (Nosko 1969, Adler 1981) and for χ2 fields in one and two dimensions (Aronowich and Adler 1986, 1988). An application of this is to detect regional changes in positron emission tomography (PET) images of blood flow in human brain, using the size of the largest connected component of the excursion set as a test statistic.
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ence test, P=.02 and P=.001, respectively) for cortical fluid volume. Accelerated ventricular volume enlargement oc- curred only in men with CDC stage C disease. Reduction in the volume of white matter was accelerated in partici- pants with CDC stage C disease compared with partici- pants with CDC stage A disease. Of the gray matter re- gions, only the caudate nucleus sustained accelerated volume loss during CDC stage C disease. Participants whose systemic disease progressed to a higher CDC stage had sig- nificantly accelerated ventricular volume increases and cau- date atrophy. Rates of cortical and subcortical fluid vol- ume increases and reductions in the volumes of white matter and the caudate nucleus were significantly related to the rate of decline in the CD4 + lymphocyte count. Conclusions: In the absence of cerebral opportunistic disease, HIV infection causes progressive atrophy within the gray and white matter in the brain. These changes were most severe in the most advanced stage of disease but were evident even in medically asymptomatic HIV- positive persons. Within the gray matter, the caudate nucleus exhibited progressive volume loss linked to dis- ease stage and the rate of decline of the CD4 + cell count. Structural brain changes can begin in the early stages of HIV infection and accelerate during advanced illness. Arch Neurol. 1998;55:161-168
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Tests comparing image sets can play a critical role in PET research, providing a yes-no answer to the question “Are two image sets different?” The statistical goal is to determine how often observed differences would occur by chance alone. We examined randomization methods to provide several omnibus test for PET images and compared these tests with two currently used methods. In the first series of analyses, normally distributed image data were simulated fulfilling the requirements of standard statistical tests. These analyses generated power estimates and compared the various test statistics under optimal conditions. Varying whether the standard deviations were local or pooled estimates provided an assessment of a distinguishing feature between the SPM and Montreal methods. In a second series of analyses, we more closely simulated current PET acquisition and analysis techniques. Finally, PET images from normal subjects were used as an example of randomization. Randomization proved to be a highly flexible and powerful statistical procedure. Furthermore, the randomization test does not require extensive and unrealistic statistical assumptions made by standard procedures currently in use.Keywords: Statistical tests; Methodology; Randomization
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Journal of Cerebral Blood Flow & Metabolism stands at the interface between basic and clinical neurovascular research, and features research on experimental, theoretical, and clinical aspects of brain circulation, metabolism and imaging. The journal is relevant to any physician or scientist with an interest in brain function, cerebrovascular disease, cerebral vascular regulation and brain metabolism, including neurologists, neurochemists, physiologists, pharmacologists, anesthesiologists, neuroradiologists, neurosurgeons, neuropathologists and neuroscientists.
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3 types of randomization tests are discussed: (a) tests for differences between independent samples, (b) tests for differences between paired sampls, and (c) tests of correlation. These tests are nonparametric yet have the power of parametric tests. The main disadvantage of randomization tests is the great amount of computation required, but this disadvantage can be overcome by using an approximation to the randomization test that reduces the computation to a practical level with very little loss of precision. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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An approximate randomization test is a randomization test in which the significance of an obtained statistic is determined by using an approximate sampling distribution consisting of a random sample of the statistics in the entire sampling distribution. Approximate randomization tests reduce the amount of computation to a practical level, and are valid tests in their own right. Even with moderate-sized approximate sampling distributions the power of an approximate randomization test is almost that of a randomization test using the entire sampling distribution. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Changes in the regional cerebral blood flow (rCBF) are often used as an indicator of changes in local neuronal activity of the brain. We present here quantitative measurements with positron emission tomography (PET) of rCBF with a freely diffusible flow tracer 15O-butanol in control and activation states, and a pixel-by-pixel statistical parametric analysis of the rCBF changes combined with a cluster analysis. Anatomically standardized rCBF activation data from 39 normal subjects were analyzed for the occurrence of clustered voxels. Noise data was obtained from repeat measurements of rCBF with the brain in the activated state and from simulations. The variance in test-control images was largest outside the skull, and large in soft tissue regions around the brain. It was moderate but inhomogenous in gray matter, and low and homogenous in white matter. A special picture was generated of the conformation of sampled data with a normal distribution. In the gray and white matter, the pixel values were fount to conform to a normal distribution, permitting calculation of Student's t-images. In the cluster analysis, activations are detected as clusters of voxels with high t-values. The clusters in activated regions, however, were considerably larger than the full width half-maximum of spatial autocorrelation function and were easily detected. Tables of the empirical Poisson-like distributions of the number of clusters of different sizes are provided, from which P values of the significance of the occurrence of clusters of different sizes can be estimated. © 1993 Wiley-Liss, Inc.
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The typical functional magnetic resonance (fMRI) study presents a formidable problem of multiple statistical comparisons (i.e, > 10,000 in a 128 x 128 image). To protect against false positives, investigators have typically relied on decreasing the per pixel false positive probability. This approach incurs an inevitable loss of power to detect statistically significant activity. An alternative approach, which relies on the assumption that areas of true neural activity will tend to stimulate signal changes over contiguous pixels, is presented. If one knows the probability distribution of such cluster sizes as a function of per pixel false positive probability, one can use cluster-size thresholds independently to reject false positives. Both Monte Carlo simulations and fMRI studies of human subjects have been used to verify that this approach can improve statistical power by as much as fivefold over techniques that rely solely on adjusting per pixel false positive probabilities.