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Statistical Parametric Maps in Functional Imaging: A General Linear Approach

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Abstract

Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis.

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... With this work, we seek to provide insights into these questions, by comparing the performance of prominent interpretation methods in a mental state decoding analysis of three functional Magnetic Resonance (fMRI) datasets. To this end, we first train DL models to accurately decode the mental states of each dataset and subsequently compare the quality of the interpretation method's explanations of the models' decoding decisions on three evaluation criteria: First, to understand how well the explanations align with the results of standard analyses of fMRI data, we compare the similarity of the explanations to the results of a standard general linear model (GLM; Friston et al., 1994) analysis of the fMRI data. We find that the explanations of sensitivity analyses are generally more similar to standard GLM contrast maps, when compared to the explanations of reference-based attributions and backward decompositions. ...
... All of our analyses were performed on trial-level statistical parametric maps (Friston et al., 1994) that were computed for each experiment trial in each dataset. Note that we refer to the resulting maps as trial-level blood-oxygen-level-dependent (BOLD) maps throughout the rest of the manuscript. ...
... To aggregate the attribution data, we first performed a standard subject-level general linear model (GLM; Friston et al., 1994) analysis of the trial-level attribution maps of each attribution method. This analysis included an indicator vector for each mental state of the dataset, and a set of nuisance regressors, namely, an indicator vector for each of the ten model training runs as well as an indicator vector for each experimental run in the data. ...
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Deep learning (DL) methods find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (such as accepting or rejecting a gamble) and brain activity, by identifying those brain regions (and networks) whose activity allows to accurately identify (i.e., decode) these states. Once DL models have been trained to accurately decode a set of mental states, neuroimaging researchers often make use of interpretation methods from explainable artificial intelligence research to understand their learned mappings between mental states and brain activity. Here, we compare the explanations of prominent interpretation methods for the mental state decoding decisions of DL models trained on three functional Magnetic Resonance Imaging (fMRI) datasets. We find that interpretation methods that capture the model's decision process well, by producing faithful explanations, generally produce explanations that are less in line with the results of standard analyses of the fMRI data, when compared to the explanations of interpretation methods with less explanation faithfulness. Specifically, we find that interpretation methods that focus on how sensitively a model's decoding decision changes with the values of the input produce explanations that better match with the results of a standard general linear model analysis of the fMRI data, while interpretation methods that focus on identifying the specific contribution of an input feature's value to the decoding decision produce overall more faithful explanations that align less well with the results of standard analyses of the fMRI data.
... Furthermore, FBNs can't be selected automatically in previous methods, and the number of FBNs needs to be set manually in a heuristic or experiential manner. Another limitation is the assumption of linearity or independence in most of existing methods [17,4,40]. For example, independent component analysis (ICA) [4,43,7] regards the identification of functional brain networks as a problem of blind source separation, that is, it is assumed that the observed signals are composed of the linear superposition of independent blind source signals. ...
... Spatial ICA [8] assumes that each blind source component is spatially independent. The generalized Linear model (GLM) [17] assumes that the observed signal is a linear superposition of the task design signal, and considers each voxel as an independent variable. Recently, some deep learning methods can learn the better nonlinear features of fMRI signals, but they still recover FBNs with linear LASSO regression [14,27,57,15,52,38,16,47,56,48]. ...
... At present, the commonly used methods to construct functional networks based on fMRI data include: general linear model (GLM) [17], independent component analysis (ICA) [4,43,8,7] and sparse dictionary learning (SDL) [40,31,20,21]. Because these methods require less data and are more stable than deep learning methods, they are widely used in clinical practices. ...
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Using deep learning models to recognize functional brain networks (FBNs) in functional magnetic resonance imaging (fMRI) has been attracting increasing interest recently. However, most existing work focuses on detecting static FBNs from entire fMRI signals, such as correlation-based functional connectivity. Sliding-window is a widely used strategy to capture the dynamics of FBNs, but it is still limited in representing intrinsic functional interactive dynamics at each time step. And the number of FBNs usually need to be set manually. More over, due to the complexity of dynamic interactions in brain, traditional linear and shallow models are insufficient in identifying complex and spatially overlapped FBNs across each time step. In this paper, we propose a novel Spatial and Channel-wise Attention Autoencoder (SCAAE) for discovering FBNs dynamically. The core idea of SCAAE is to apply attention mechanism to FBNs construction. Specifically, we designed two attention modules: 1) spatial-wise attention (SA) module to discover FBNs in the spatial domain and 2) a channel-wise attention (CA) module to weigh the channels for selecting the FBNs automatically. We evaluated our approach on ADHD200 dataset and our results indicate that the proposed SCAAE method can effectively recover the dynamic changes of the FBNs at each fMRI time step, without using sliding windows. More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.
... All variables met the assumptions of normality, which were evaluated using the z-score of the skewness and kurtosis statistics (Field, 2013). Therefore, to assess the agreement (mean difference, correlation, and proportional bias) between the two-marker methods (HEEL-TOE and HEEL-MET 5 ) and the V3D-REF in measuring the FSA, three statistical approaches were employed. ...
... Therefore, the t-statistic (SPM{t}) was calculated on a continuous level. To test the null hypothesis (no difference between the methods), a critical threshold (t*) was calculated for each comparison (Friston et al., 1994). Thus, for SPM{t} values where the upper or lower t* thresholds were exceeded, the null hypothesis was rejected. ...
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A simple and accurate method of determining foot strike angle (FSA) during running can simplify data collections and validations of wearable sensors. The purpose of this study was to determine the validity of two simplified methods for estimating FSA and foot angle (throughout the ground contact) from three-dimensional kinematics. Markers were placed on the heel and head of the second metatarsal (HEEL-TOE) or on the lateral side of the head of the fifth metatarsal (HEEL-MET5). When compared to the reference foot segment, the HEEL-TOE method performed similarly with a minimal mean difference (0.28° [0.19°,0.36°], p < 0.001), a high Pearson's r (r = 0.994; p < 0.001), and low bias (-0.20°±1.05°). Alternatively, the HEEL-MET5 method underestimated FSA: mean difference = 4.28° [4.07°,4.91°] (p < 0.001), Pearson's r = 0.968 (p < 0.001), and bias = -4.58°±2.61°. Throughout the contact phase, significant SPM cluster regions were identified, indicating that the HEEL-MET5 method underestimated the angle of the foot for all foot strike patterns in the first 23-34% of the stance (p < 0.025). This study supports the idea that the HEEL-TOE method can be used as a simplified method for determining FSA from 3D kinematics. Researchers should proceed with caution when employing the HEEL-MET5 method, as it is likely underestimating FSA due to foot inversion in the early stance phase.
... Remaining differences will be due to differences in amplitude rather than due to temporal shifts. SPM ensures that the familywise error rate stays at the desired overall significance level (Friston et al., 1994;Robinson et al., 2015). This is important since time correlations present within data can overestimate significance of multiple tests across time, also known as multiple comparisons problem. ...
... To highlight differences in amplitude rather than differences due to shifted event times, we normalize time by DTW with one randomly drawn group sample each as reference signal (Honert and Pataky, 2021;Weiske et al., 2021). At each time step, a one-way Analysis of Variance (ANOVA) with SPM alpha-level correction identifies significantly different F-statistics (Friston et al., 1994;Friston, 2007;Robinson et al., 2015). SPM corrects the test at each time step to account for estimated time correlations present in the data with a target familywise error rate of 5%. ...
Article
Climbing stairs can become a daily obstacle for elderly people, and an exoskeleton can assist here. However, the exoskeletons that are designed to assist stair climbing are actuated in different ways. To find a minimal actuation configuration, we identify the assist phases by evaluating the power deficit of 11 healthy but weak elderly people (72.4 ± 2.1 years; 69–76 years; 1.67 ± 0.10 m; 74.88 ± 14.54 kg) compared to 13 younger people (24.0 ± 1.8 years; 22–28 years; 1.74 ± 0.10 m; 70.85 ± 11.91 kg) in a biomechanical study and discuss moment characteristics. Three-dimensional kinematics and ground reaction forces were collected, and kinematics, kinetics, and power characteristics of each subject for ascent and descent were calculated using inverse dynamics. Significant differences for power between both groups were assessed with statistical parametric mapping method using dynamic time warping. During ascent, the largest significant power deficit of the elderly subjects occurs in the single stance phase (SSP) during pull-up in the knee joint. During descent, significant mean power deficits of 0.2 and 0.8 W/kg for the highest deficit occur in the ankle joint in the beginning of the SSP and also in the knee joint in the same phase. Therefore, an exoskeleton should address the power deficit for knee extension (ascent: 1.0 ± 0.9 W/kg; descent: 0.3 ± 0.2 W/kg) and could assist the ankle during ascent and descent by an additional plantar flexion moment of 0.2 Nm/kg each.
... Image analysis was performed using SPM12 (Friston et al., 1995) and custom software. The T1 and rsfMRI images of the CRPS subjects with pain restricted to the left upper limb (or the more intense pain in the left upper limb) were left-right reflected across the midline on the y-axis before data processing. ...
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Complex regional pain syndrome (CRPS) is a painful condition commonly accompanied by movement disturbances and often affects the upper limbs. The basal ganglia motor loop is central to movement, however, non‐motor basal ganglia loops are involved in pain, sensory integration, visual processing, cognition, and emotion. Systematic evaluation of each basal ganglia functional loop and its relation to motor and non‐motor disturbances in CRPS has not been investigated. We recruited 15 upper limb CRPS and 45 matched healthy control subjects. Using functional magnetic resonance imaging, infraslow oscillations (ISO) and resting‐state functional connectivity in motor and non‐motor basal ganglia loops were investigated using putamen and caudate seeds. Compared to controls, CRPS subjects displayed increased ISO power in the putamen contralateral to the CRPS affected limb, specifically, in contralateral putamen areas representing the supplementary motor area hand, motor hand, and motor tongue. Furthermore, compared to controls, CRPS subjects displayed increased resting connectivity between these putaminal areas as well as from the caudate body to cortical areas such as the primary motor cortex, supplementary and cingulate motor areas, parietal association areas, and the orbitofrontal cortex. These findings demonstrate changes in basal ganglia loop function in CRPS subjects and may underpin motor disturbances of CRPS. This study is the first systematic evaluation of functional connectivity of basal ganglia motor and non‐motor territories in CRPS. In CRPS, there is increased ISOs and resting functional connectivity in motor but not in non‐motor basal ganglia territories (except caudate body functional connectivity) compared to controls.
... Medical System, Milwaukee, WI, USA) and processed using SPM12 toolbox (Friston et al., 1994) (details see Supplementary Materials). ...
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Background: Major depression (MDD) and generalized anxiety disorder (GAD) have become one of the leading global causes of disability and both are characterized by marked interpersonal and social impairments. However, despite a high comorbidity and overlapping social-emotional deficits it remains unclear whether MDD and GAD share a common neural basis during interpersonal processing. Methods: This study combined an emotional face processing paradigm with fMRI and dimensional and categorical analyses in a sample of unmedicated MDD and GAD patients (N = 72) as well as healthy controls (N = 35). Results: No group differences were found in categorical analyses. However, the dimensional analyses revealed that dorsolateral prefrontal cortex (dlPFC) reactivity to sad facial expressions was positively associated with depressive, yet negatively associated with GAD symptom load in the entire sample. On the network level depression symptom load was positively associated with functional connectivity between the bilateral amygdala and a widespread network including the anterior cingulate and insular cortex. Limitations: Sex differences were not examined in the present study and some patients exhibited depression-GAD comorbidity. Conclusions: Together, these findings suggest that the dlPFC - engaged in cognitive and emotional processing - exhibits symptom- and emotion-specific alteration during interpersonal processing. Dysregulated communication between amygdala and core regions of the salience network may represent MDD-specific neural dysregulations.
... Imaging data were processed with SPM 12 (Statistical Parametric Mapping; Wellcome Department of Cognitive Neurology, London, United Kingdom 3 ) implemented in Matlab (R2014a, MathWorks, Inc., Natick, MA, United States) (Friston et al., 1994). The first five volumes of each functional time series were discarded to allow for T1 equilibration. ...
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In humans, the neuropeptide oxytocin promotes both attraction toward and bonds with romantic partners, although no studies have investigated whether this extends to the perceived attractiveness of flirtatious language. In a within-subject, randomized double-blind placebo-controlled behavior and functional magnetic resonance imaging (fMRI) paradigm ( https://clinicaltrials.gov/show/NCT03144115 ), 75 women rated the attractiveness of either a male face alone or paired with a verbal compliment which varied in terms of topic (women or landscapes) and figurativeness (novel or conventional metaphors or literal expressions). Subjects were tested in fertile and luteal phases of their cycle and on both occasions received either 24 IU intranasal oxytocin or placebo. Results showed that, whereas under placebo women in the fertile phase rated the facial attractiveness of men producing novel metaphorical compliments higher than in their luteal phase, following oxytocin treatment they did not. Correspondingly, under oxytocin the faces of individuals producing novel metaphorical compliments evoked greater responses in brain regions involved in processing language (middle frontal gyrus) and cognitive and emotional conflict (posterior middle cingulate and dorsal anterior cingulate) but reduced functional connectivity between the dorsal anterior cingulate and right orbitofrontal and medial frontal gyri. Thus, sex hormones and oxytocin may have opposite effects in regulating mate selection in women during their fertile phase. Novel metaphorical compliments convey a greater sexual than bonding intention and thus while sex hormones at mid-cycle may promote attraction to individuals communicating sexual rather than bonding intent, oxytocin may bias attraction away from such individuals through increasing cognitive and emotional conflict responses toward them.
... Univariate analysis was conducted using the SPM toolbox with general linear models (GLMs) (Friston et al. 1994). In the GLM analysis, face stimuli blocks were modeled with a boxcar function and convolved with a standard hemodynamic response function. ...
Article
Self-other distinction is crucial for human interaction. Although with conflicting results, studies have found that oxytocin (OT) sharpens the self-other perceptual boundary. However, little is known about the effect of OT on self-other perception, especially its neural basis. Moreover, it is unclear whether OT influences self-other discrimination when the other is a child or an adult. This double-blind, placebo-controlled study investigated the effect of OT on self-face perception at the behavioral and neural levels. For the stimuli, we morphed participants' faces and child or adult strangers' faces, resulting in 4 conditions. After treatment with either OT or placebo, participants reported whether a stimulus resembled themselves while being scanned using functional magnetic resonance imaging (fMRI). Behavioral results showed that people judged adult-morphed faces better than child-morphed faces. Moreover, fMRI results showed that the OT group exhibited increased activity in visual areas and the inferior frontal gyrus for self-faces. This difference was more pronounced in the adult-face condition. In multivariate fMRI and region of interest analyses, better performance in the OT group indicated that OT increased self-other distinction, especially for adult faces and in the left hemisphere. Our study shows a significant effect of OT on self-referential processes, proving the potential effect of OT on a left hemisphere self-network.
... The fMRI data preprocessing analyses were performed using spm8 software (The Wellcome Centre for Human Neuroimaging, London, UK) [31]. The raw data were examined for excessive motion as the skull vibration induced by the pneumatic taps could be a potential source of motion artifacts. ...
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Vibrotactile sensory augmentation (SA) decreases postural sway during real-time use; however, limited studies have investigated the long-term effects of training with SA. This study assessed the retention effects of long-term balance training with and without vibrotactile SA among community-dwelling healthy older adults, and explored brain-related changes due to training with SA. Sixteen participants were randomly assigned to the experimental group (EG) or control group (CG), and trained in their homes for eight weeks using smart-phone balance trainers. The EG received vibrotactile SA. Balance performance was assessed before, and one week, one month, and six months after training. Functional MRI (fMRI) was recorded before and one week after training for four participants who received vestibular stimulation. Both groups demonstrated significant improvement of SOT composite and MiniBESTest scores, and increased vestibular reliance. Only the EG maintained a minimal detectable change of 8 points in SOT scores six months post-training and greater improvements than the CG in MiniBESTest scores one month post-training. The fMRI results revealed a shift from activation in the vestibular cortex pre-training to increased activity in the brainstem and cerebellum post-training. These findings showed that additional balance improvements were maintained for up to six months post-training with vibrotactile SA for community-dwelling healthy older adults.
... In this research, the peak force and accumulated force of quadriceps muscle before and after hamstring optimal lengths changed were calculated, and the accumulated force was defined as the accumulation of quadriceps muscle force overtime of a gait cycle ( Force dt). To analyze the effect of the hamstring optimal lengths' changes on the quadriceps muscle force throughout the gait cycle, the paired statistical parametric mapping (SPM) t-tests [30,31] were used from an open-source spm1d package (https://www.spm1d.org) [32] in MATLAB (R2019a, e Mathworks Inc., Natick, USA). ...
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Background: The relationship between hamstring flexibility and the risk of OSD continues to be a debate, and whether hamstring stretching exercises should be considered as one of the conservative treatments of OSD is still unclear. Objectives: To investigate the relationship between hamstring flexibility and the risk of OSD by assessing the changes of loading on the tibial tuberosity caused by the changes of hamstring optimal lengths. Methods: Experimental data of a young adult running at 4 m/s were used, which were collected by an eight-camera motion capture system together with an instrumented treadmill. Muscle forces were estimated in OpenSim when hamstring optimal lengths changed in the range of 70-130% of the control case in 5% increments. The force and accumulated force of quadriceps muscle were calculated to evaluate the impact of hamstring optimal lengths on the loading on tibial tuberosity. The changes in muscle forces throughout the gait cycle were compared by using statistical parametric mapping (SPM). The average peak force and accumulated force of five gait cycles were compared. Results: Although the maximum force of the quadriceps muscle was slightly affected by changes in hamstring optimal lengths, the accumulated force of quadriceps muscle increased by 21.97% with hamstring optimal lengths decreased by 30% of the control case. The increase of the muscle force mainly occurred in the early stance phase and terminal swing phase (P < 0.05). However, when hamstring optimal lengths were longer than the control, it had a little effect on accumulated force of quadriceps muscle. Conclusions: The results of this study indicate that a shorter hamstring optimal length, which means lack of flexibility, can cause a high accumulated force on tibial tuberosity, thus increasing the risk of OSD. Hamstring stretching exercise is only effective for people with lack of hamstring flexibility.
... Differences between normal walking and voluntary toe-walking were analyzed through statistical parametric mapping (SPM; www.spm1D.org, v0.43) (Friston et al., 1994;Pataky, 2012) for joint kinematics, joint moments, muscle contributions to the joint moments, muscle forces, and for support and progression moments. Due to the reduced number of tested subjects (n = 9), non-parametric (Pataky et al., 2015) two-tailed paired t-tests were used to identify statistically significant differences between the two walking modalities. ...
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Toe-walking characterizes several neuromuscular conditions and is associated with a reduction in gait stability and efficiency, as well as in life quality. The optimal choice of treatment depends on a correct understanding of the underlying pathology and on the individual biomechanics of walking. The objective of this study was to describe gait deviations occurring in a cohort of healthy adult subjects when mimicking a unilateral toe-walking pattern compared to their normal heel-to-toe gait pattern. The focus was to characterize the functional adaptations of the major lower-limb muscles which are required in order to toe walk. Musculoskeletal modeling was used to estimate the required muscle contributions to the joint sagittal moments. The support moment, defined as the sum of the sagittal extensive moments at the ankle, knee, and hip joints, was used to evaluate the overall muscular effort necessary to maintain stance limb stability and prevent the collapse of the knee. Compared to a normal heel-to-toe gait pattern, toe-walking was characterized by significantly different lower-limb kinematics and kinetics. The altered kinetic demands at each joint translated into different necessary moment contributions from most muscles. In particular, an earlier and prolonged ankle plantarflexion contribution was required from the soleus and gastrocnemius during most of the stance phase. The hip extensors had to provide a higher extensive moment during loading response, while a significantly higher knee extension contribution from the vasti was necessary during mid-stance. Compensatory muscular activations are therefore functionally required at every joint level in order to toe walk. A higher support moment during toe-walking indicates an overall higher muscular effort necessary to maintain stance limb stability and prevent the collapse of the knee. Higher muscular demands during gait may lead to fatigue, pain, and reduced quality of life. Toe-walking is indeed associated with significantly larger muscle forces exerted by the quadriceps to the patella and prolonged force transmission through the Achilles tendon during stance phase. Optimal treatment options should therefore account for muscular demands and potential overloads associated with specific compensatory mechanisms.
... Functional volumes were then resliced to 1 mm 3 isotropic and smoothed with a 1.5 mm (FWHM) Gaussian kernel using SPM12. A general linear model (GLM) was used for estimating the response amplitude at each voxel (using SPM12) following previously described procedures ( Friston et al., 1995 ;Vanduffel et al., 2001 ). Stimulus conditions were presented as a boxcar model convolving with a MION hemodynamic response function ( Vanduffel et al., 2001 ). ...
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While brain research over the past decades has shed light on the neural correlates of social cognition and behavior in human and non-human primates, most of this research has been performed in virtual settings requiring subjects to observe pictures or recorded videos instead of observing or interacting with another real-live individual. Here we present a two-monkey fMRI setup, allowing examining whole brain responses in macaque monkeys while they observe or interact face-to-face with another real-live conspecific. We tested this setup by comparing overall brain responses during observation of conspecific hand actions in a virtual (observation of recorded videos of actions) or live context (observation of a real-live conspecific performing actions). This dyadic monkey fMRI setup allows examining brain-wide responses in macaque monkeys during different aspects of social behavior, including observation of real-live actions and sensations, social facilitation, joint-attention and social interactions.
... In detail, brain states represent the whole-brain activity at a given time point, which occur repeatedly during certain condition and are reproducible across subjects. The major advantage of this method, in comparison to standard brain activation analysis methods based on General Linear Model (Friston et al. 1994), is the ability to get a deep insight into the brain dynamics in the fast-changing environment or under rapid stimuli, which makes it suitable to track changes in brain activity during psychedelic experiences. Here, we applied the K-Means clustering algorithm (Le Cam and Neyman 1967;Lloyd 1982;Goutte et al. 1999;Allen et al. 2014;Cornblath et al. 2020) to identify brain states and to compare their dynamics during resting-state and music listening. ...
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Psychedelics are getting closer to being widely used in clinical treatment. Music is known as a key element of psychedelic-assisted therapy due to its psychological effects, specifically on the emotion, meaning-making and sensory processing. However, there is still a lack of understanding in how psychedelics influence brain activity in experimental settings involving music listening. The main goal of our research is to investigate the effect of music, as a part of "setting", on the brain states dynamics after lysergic acid diethylamide (LSD) intake. We used an open dataset, where a group of 15 participants underwent two scanning sessions under LSD influence and under placebo. Every scanning session contained three runs: two resting-state runs separated by one run with music listening. We applied the K-Means clustering to identify the repetitive patterns of brain activity, so-called brain states. For further analysis, we calculated states' dwell time, fractional occupancy and transition probability. We found that the brain states dynamics during the resting-state and listening to music on both LSD and placebo does not differ significantly. Furthermore, we found that the music itself could potentially have a long-term influence on the resting-state, in particular on the states involving task-positive networks. Collectively, these findings suggest that the whole-brain states' dynamics during psychedelic experience and placebo is relatively stable, however, music, as a crucial element of setting, can potentially have an influence on the subject's resting-state. Further studies should replicate these results on a larger sample size.
... The images were visually checked and validated regarding normalization and registration success. Statistical analysis was based on a voxel-wise least-squares estimation using the general linear model for serially autocorrelated observations 42 . Because the current study used a blocked fMRI design, a boxcar function, convolved with a canonical HRF without derivatives was used to model the BOLD response. ...
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Behavioural studies investigating the relationship between Executive Functions (EFs) demonstrated evidence that different EFs are correlated with each other, but also that they are partially independent from each other. Neuroimaging studies investigating such an interrelationship with respect to the functional neuroanatomical correlates are sparse and have revealed inconsistent findings. To address this question, we created four tasks derived from the same basic paradigm, one each for updating, inhibition, switching, and dual-tasking. We assessed brain activity through functional magnetic resonance imaging (fMRI) in twenty-nine participants while they performed the four EF tasks plus control tasks. For the analysis, we first determined the neural correlates of each EF by subtracting the respective control tasks from the EF tasks. We tested for unity in EF tasks by calculating the conjunction across these four “EF-minus-control” contrasts. This identified common areas including left lateral frontal cortices [middle and superior frontal gyrus (BA 6)], medial frontal cortices (BA 8) as well as parietal cortices [inferior and superior parietal lobules (BA 39/7)]. We also observed areas activated by two or three EF tasks only, such as frontoparietal areas [e.g., SFG (BA8) right inferior parietal lobule (BA 40), left precuneus (BA 7)], and subcortical regions [bilateral thalamus (BA 50)]. Finally, we found areas uniquely activated for updating [bilateral MFG (BA 8) and left supramarginal gyrus (BA 39)], inhibition (left IFG BA 46), and dual-tasking [left postcentral gyrus (BA 40)]. These results demonstrate that the functional neuroanatomical correlates of the four investigated EFs show unity as well as diversity.
... Synthetic and experimental data were analyzed using the general linear model (GLM). This method was developed for the analysis of fMRI data by Ref. 85 and extended to fNIRS. It expresses the fNIRS signal as a linear combination of several variables, known as regressors, plus an error term. ...
Article
Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated ( HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on HbO 2 only. Previous work has shown that HbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both HbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of HbO 2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combine HbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [ HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on HbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using HbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.
... The analysis was performed with SPM12 (Wellcome Trust Center for Neuroimaging, London, UK) using a general linear model (GLM) with a canonical hemodynamic response function (HRF) to assess task vs rest activations. 25 To identify auditory responses to the verbal commands, a new condition block design was created with an impulse of 0 length at the beginning of each verbal command (both start and stop commands) and convolved with an HRF. For each scan, contrasts between periods of active imagery with periods of rest were calculated, with 8 periods of imagery and rest for P1 and P2. ...
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Objective Following severe brain injury, up to 16% of adults showing no clinical signs of cognitive function nonetheless have preserved cognitive capacities detectable via neuroimaging and neurophysiology; this has been designated cognitive-motor dissociation (CMD). Pediatric medicine lacks both practice guidelines for identifying covert cognition, and epidemiologic data regarding CMD prevalence. Methods We applied a diverse battery of neuroimaging and neurophysiological tests to evaluate two adolescents (aged 15 and 18) who had shown no clinical evidence of preserved cognitive function following brain injury at age 9 and 13 respectively. Clinical evaluations were consistent with minimally conscious state (minus) and vegetative state, respectively. Results Both subjects’ EEG, and one subject’s fMRI, provided evidence that they could understand commands and make consistent voluntary decisions to follow them. Both subjects’ EEG demonstrated larger-than-expected responses to auditory stimuli, and intact semantic processing of words in context. Interpretation These converging lines of evidence lead us to conclude that both subjects had preserved cognitive function dissociated from their motor output. Throughout the 5+ years since injury, communication attempts and therapy had remained uninformed by such objective evidence of their cognitive abilities. Proper diagnosis of CMD is an ethical imperative. Children with covert cognition reflect a vulnerable and isolated population; the methods outlined here provide a first step in identifying such persons to advance efforts to alleviate their condition.
... 24 Image registration was performed using the clinical toolbox 25 in SPM12. 26 We avoided distortion and warping of the damaged tissue by applying cost function masks to the areas damaged by stroke prior to registration. 27 The registered VOIs were compared to the original images to ensure accuracy. ...
Article
Background Motor impairment in the arms is common after stroke and many individuals participate in therapy to improve function. It is assumed that individuals with stroke can adapt and improve their movements using feedback that arises from movement or is provided by a therapist. Here we investigated visuomotor adaptation in individuals with sub-acute and chronic stroke. Objective We examined the impact of the stroke-affected arm (dominant or non-dominant), time post-stroke, and relationships with clinical measures of motor impairment and functional independence. Methods Participants performed reaching movements with their arm supported in a robotic exoskeleton. We rotated the relationship between the motion of the participant’s hand and a feedback cursor displayed in their workspace. Outcome measures included the amount that participants adapted their arm movements and the number of trials they required to adapt. Results Participants with stroke (n = 36) adapted less and required more trials to adapt than controls (n = 29). Stroke affecting the dominant arm impaired the amount of adaptation more than stroke affecting the non-dominant arm. Overall, 53% of participants with stroke were impaired in one or more measures of visuomotor adaptation. Initial adaptation was weakly correlated with time post-stroke, and the amount of adaptation correlated moderately with clinical measures of motor impairment and functional independence. Conclusion Our findings reveal impairments in visuomotor adaptation that are associated with motor impairment and function after stroke. Longitudinal studies are needed to understand the relationship between adaptation and recovery attained in a therapy setting.
... Finally, this data was adjusted for global signal fluctuations, also known as global scaling to account for differences in system responses across multiple sessions. A general linear model analysis (Friston et al., 1994) of the combined sessions included the motion parameters, the voxel-wise response estimates and the regression coefficients. The t-values for two contrasts (Figure vs Control, Sound vs Silence) were calculated. ...
Thesis
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Natural sensory scenes are often very complex, with a multitude of overlapping objects in space and time. In order to direct behaviour, a critical aspect of everyday perception is the segregation and grouping of relevant features from those scenes, known as figure-ground segregation. The neurobiological basis of auditory figure-ground processing is poorly understood. To gain insights into different aspects of this process, I have investigated the behavioural, systemic and neuronal mechanisms the brain uses to segregate and group temporally coherent elements from a complex acoustic scene in macaque monkeys. This thesis presents the result of this research in five chapters: Chapter 1 reviews the fundamental basics of auditory scene analysis and the auditory system. Chapter 2, 3 and 4 present experimental work and cover figure detection behaviour (Chapter 2), systemic organisation of figure-ground analysis (Chapter 3) and the underlying neuronal mechanisms (Chapter 4). Finally, Chapter 5 discusses and interprets the results in the context of previous research. In summary, this work establishes that macaques are an excellent animal model for auditory scene analysis and provides new evidence of the cortical response mechanisms during auditory figure-ground segregation. I show that macaques have not only similar detection performance to humans but that the areal organisation measured with fMRI is comparable. Furthermore, I demonstrate robust effects on neuronal firing rates in response to auditory figures across the cortical hierarchy. Lastly, this thesis establishes neuronal differences in figure processing between anterior and posterior auditory cortical fields.
... Conventional neuroimaging data analysis typically aims at using experimental conditions or behavioral responses to predict measured signals ("generative models", e.g. general linear models; Friston et al., 1994;Friston and Price, 2001) or to decode experimental inputs or behavioral outputs from measured signals ("decoding algorithms", e.g. support vector machines; Cox and Savoy, 2003;Haynes, 2015). ...
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The data acquired during a functional magnetic resonance imaging (fMRI) experiment usually comprise experimental conditions, brain signals and behavioral responses. This reflects the underlying causal flow where the experimental conditions evoke brain responses that in turn result in behavior. In multivariate analyses, a common approach is to focus on the second step of that chain and to decode behavioral responses from brain signals. However, a different approach would be to first reconstruct the experimental conditions from brain signals and in turn use these to predict behavior. While this indirect approach would go against the causal chain of events, it might work better under certain circumstances, especially when the experimental conditions evoke much stronger measurable brain signals than the overt motor behavior. Here we tested this question directly by assessing the various mappings between conditions, brain signals and behavior in an open dataset. We found that the path of first decoding experimental conditions works surprisingly well, even though in our example data set, it is still outperformed by directly decoding the behavior from brain responses.
... We defined brain regions as network nodes using the automated anatomical labeling atlas 36 , which consists of 78 cortical and 12 subcortical regions, excluding cerebellum regions. We registered them onto the DTI space of each subject using Statistical Parametric Mapping software (version 12, SPM12) 37 . The overall procedure is briefly introduced as follows. ...
Article
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Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are two representative chronic inflammatory demyelinating disorders of the central nervous system. We aimed to determine and compare the alterations of white matter (WM) connectivity between MS, NMOSD, and healthy controls (HC). This study included 68 patients with relapsing–remitting MS, 50 with NMOSD, and 26 HC. A network-based statistics method was used to assess disrupted patterns in WM networks. Topological characteristics of the three groups were compared and their associations with clinical parameters were examined. WM network analysis indicated that the MS and NMOSD groups had lower total strength, clustering coefficient, global efficiency, and local efficiency and had longer characteristic path length than HC, but there were no differences between the MS and NMOSD groups. At the nodal level, the MS group had more brain regions with altered network topologies than did the NMOSD group when compared with the HC group. Network alterations were correlated with Expanded Disability Status Scale score and disease duration in both MS and NMOSD groups. Two distinct subnetworks that characterized the disease groups were also identified. When compared with NMOSD, the most discriminative connectivity changes in MS were located between the thalamus, hippocampus, parahippocampal gyrus, amygdala, fusiform gyrus, and inferior and superior temporal gyri. In conclusion, MS patients had greater network dysfunction compared to NMOSD and altered short connections within the thalamus and inferomedial temporal regions were relatively spared in NMOSD compared with MS.
... The trials were separated by a jittered inter-trial interval of 4-6s which served as low-level baseline. The first GLM model examined effects of treatment on emotional experience and modelled presentation of each stimulus according to the valence condition (positive, negative, neutral) using an event-related design and convolution with the hemodynamic response function (53). Rating periods (arousal, valence) and six head motion parameters were included as covariates. ...
Article
BACKGROUND Exaggerated arousal and dysregulated emotion-memory interactions are key pathological dysregulations that accompany the development of post-traumatic stress disorder (PTSD). Current treatments for PTSD are of moderate efficacy and preventing the dysregulations already during exposure to threatening events may attenuate the development of PTSD-symptomatology. METHODS In a preregistered double-blind, between-subject, placebo-controlled pharmaco-fMRI design, the present proof-of-concept study examined the potential of a single dose of angiotensin II type 1 receptor (AT1R) antagonist losartan (LT) to attenuate the mnemonic advantage of threatening stimuli and the underlying neural mechanism via combining an emotional subsequent memory paradigm with LT (n=29) or placebo treatment (n=30) and a surprise memory test after 24h washout. RESULTS LT generally improved memory performance and abolished emotional memory enhancement for negative yet not positive material while emotional experience during encoding remained intact. LT further suppressed hippocampus activity during encoding of subsequently remembered negative stimuli. On the network level LT reduced coupling between hippocampus and basolateral amygdala during successful memory formation of negative stimuli. CONCLUSIONS Our findings suggest that LT may have the potential to attenuate memory formation for negative yet not positive information by decreasing hippocampus activity and its functional coupling strength with amygdala. These findings suggest a promising potential of LT to prevent preferential encoding and remembering of negative events, a mechanism that could prevent the emotion-memory dysregulations underlying the development of PTSD-symptomatology.
... All EPI images were automatically rigid-body transformed to correct for head motion and a distortion correction algorithm was applied [56]. Preprocessing and statistical analysis of the functional data was performed with the statistical parametric mapping software SPM12 (Wellcome Trust Centre of Imaging Neuroscience, London; for details, see [57]). The first two volumes of each run were disregarded and an artifact detection algorithm (ArtRepair toolbox, SPM) was applied to detect head motion and possible spiking artifacts. ...
Article
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Background The understanding of the cerebral neurobiology of anorexia nervosa (AN) with respect to state- versus trait-related abnormalities is limited. There is evidence of restitution of structural brain alterations with clinical remission. However, with regard to functional brain abnormalities, this issue has not yet been clarified. Methods We compared women with AN (n = 31), well-recovered female participants (REC) (n = 18) and non-patients (NP) (n = 27) cross-sectionally. Functional magnetic resonance imaging was performed to compare neural responses to food versus non-food images. Additionally, affective ratings were assessed. Results Functional responses and affective ratings did not differ between REC and NP, even when applying lenient thresholds for the comparison of neural responses. Comparing REC and AN, the latter showed lower valence and higher arousal ratings for food stimuli, and neural responses differed with lenient thresholds in an occipital region. Conclusions The data are in line with some previous findings and suggest restitution of cerebral function with clinical recovery. Furthermore, affective ratings did not differ from NP. These results need to be verified in intra-individual longitudinal studies.
... The stimulation paradigms and protocols are described in Methods. Functional responses to these tasks were extracted using the general linear model (GLM) 12 . The extracted functional responses were overlaid on the T1-weighted and MRA images after co-registering the MRA images with the PACT images. ...
Preprint
Herein we report the first in-human transcranial imaging of brain function using photoacoustic computed tomography. Functional responses to benchmark motor tasks were imaged on both the skull-less and the skull-intact hemispheres of a hemicraniectomy patient. The observed brain responses in these preliminary results demonstrate the potential of photoacoustic computed tomography for achieving transcranial functional imaging.
... The threshold is statistically arbitrary, since it is repeated in each permutation (Friston et al., 1994) but having a narrower definition of clusters makes them far easier to interpret in terms of their spatial extent. The permutation test produces null-distributions of cluster t-statistics based on shuffling data, which is then compared to the actual observed cluster t-values. ...
Thesis
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This thesis investigates the impact of socioeconomic status (SES) on the neurophysiology of children’s brains. Human beings develop within a variety of social and economic environments. This variability is inevitably reflected in cognition and associated brain activity. This includes stark negative impacts on the lower end of this spectrum. Research is often limited to one measure of SES, such as income or housing. There is also a tendency to limit analysis to one outcome, like a cognitive test or a particular form of brain scan. However, both SES and neurocognitive development are multi-faceted. If we are to understand their interactions fully, this complexity must be considered. I endeavour to address this complexity in the current thesis, by considering multiple measures of SES across structural, functional and task-based neuroimaging. I apply data-driven techniques including general linear modelling, auto-regressive models, and graph matching. Chapter 1 reviews the current literature on SES and development. I put forward a multi-level approach to build upon previous work. In Chapter 2 I compare different methods of modelling brain networks and contrast their suitability for capturing SES related variance. I find a distributed network of connections which relate to different elements of SES. I also show that functional neurophysiological methods are superior in capturing this variance. In Chapter 3, I investigate how neurophysiological activity during a passive phonological task predicts SES. I find that later processing is specifically related to subjective parental ratings of SES. In Chapter 4 I extend this approach to an actively involved visual working memory task and find differential associations with objective and subjective measures. In Chapter 5 I integrate these findings with existing theories and models. I find my results support theories connecting SES, inhibition, and language processing. I also reflect on the many distributed associations that do not fit these parsimonious models. Most importantly, this thesis showcases a new approach to SES research in cognitive neuroscience, and the importance of considering SES as multi-factorial.
... We distinguished five trial conditions: HAN, HAP, LAN, LAP, and FIX. A statistical model was constructed for each individual subject using a general linear model (GLM) with image onset in each trial convolved with a canonical hemodynamic response function and with its temporal derivative for entry as regressors in the model (Friston et al., 1995). Realignment parameters in all six dimensions were entered in the model. ...
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Background Big wave surfers are extreme sports athletes who expose themselves to life-threatening risk when training and competing. Little is known about how and why extreme sports athletes choose to participate in their chosen sports. This exploratory study investigated potential neurophysiological and psychometric differences between big and non-big wave surfers. Methods Thirteen big wave surfers (BWS) and 10 non-big wave surfers (CON) viewed a series of images from the International Affective Picture System (IAPS) while undergoing brain functional magnetic resonance imaging (fMRI). The Fear Schedule Survey-III, Arnett Inventory of Sensation Seeking, Discrete Emotions Questionnaire, and Positive and Negative Affect Schedule were also completed. Results The BWS group demonstrated higher blood-oxygen level-dependent (BOLD) signal change in the insula, visual cortex, and periaqueductal gray, whereas the CON group displayed increased hypothalamus activation in response to high amplitude negative-valence (HAN) image presentation. Psychophysiological interaction (PPI) analyses found CON showed significant interactions between frontal and temporal cortical regions as well as between the hypothalamus and the insula, frontal, and temporal cortices during HAN image presentation that were not seen in BWS. No differences between groups were found in their responses to the questionnaires. Conclusion Our findings demonstrate significant differences in brain activation between BWS and CON in response to the presentation of HAN IAPS images, despite no significant differences in scores on psychometric questionnaires.
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Sleep spindles (8 - 16 Hz) are transient electrophysiological events during non-rapid eye movement sleep. While sleep spindles are routinely observed in the cortex using scalp electroencephalography (EEG), recordings of their thalamic counterparts have not been widely studied in humans. Based on a few existing studies, it has been hypothesized that spindles occur as largely local phenomena. To see whether they also facilitate thalamocortical communication, we investigated intra-thalamic and thalamocortical spindle co-occurrence. We obtained scalp EEG and thalamic recordings from 7 patients that received bilateral deep brain stimulation (DBS) electrodes to the anterior thalamus for the treatment of drug resistant focal epilepsy. Spindles were categorized into subtypes based on their main frequency (i.e., slow (10±2 Hz) or fast (14±2 Hz)) and their level of thalamic involvement (spanning one channel, or spreading uni- or bilaterally within the thalamus). For the first time, we contrasted observed spindle patterns with permuted data to estimate random spindle co-occurrence. We found that multichannel spindle patterns were systematically coordinated at the thalamic and thalamocortical level. Importantly, distinct topographical patterns of thalamocortical spindle overlap were associated with slow and fast subtypes of spindles. These observations provide further evidence for coordinated spindle activity in thalamocortical networks.
Article
Ankle osteoarthritis is a chronic debilitating disease marked by cartilage breakdown, pain and significant biomechanical impairment of the entire lower limb. Total ankle replacement (TAR) has been encouraged during the last decade as it has the potential to maintain the existing pre-operative ankle range of motion and to protect the more distally located joints of the foot. Three-dimensional gait analysis using a multi-segment foot model can provide an objective analysis of TAR for the treatment of end-stage ankle osteoarthritis. Thirty-six patients suffering from post-traumatic end-stage ankle osteoarthritis were evaluated before and after TAR. A four-segment kinematic foot model was used to calculate intrinsic foot joint kinematics during gait. Spatio-temporal parameters were also assessed. Kinematic results were compared to a control group of asymptomatic subjects. Differences in waveform patterns were mainly limited to dorsi-/plantarflexion inter-segment angles. At loading response, the Shank-Calcaneus plantarflexion angles as well as the Calcaneus-Midfoot dorsiflexion angle increased slightly in post-operative condition. During propulsion, an increase in Hallux-Metatarsus dorsiflexion angle was observed. Pain improved after surgery as supported by increased spatio-temporal parameters. While multi-segment foot and ankle kinematics were improved, they remained impaired compared to control values. This study confirms that TAR maintains the residual pre-operative range of motion after surgery from midstance to propulsion. Furthermore, the results suggest that the kinematic behavior of the foot joints distal to the affected ankle joint also improves post-operatively. The outcome of this study further emphasizes the clinical relevance of multi-segment foot modeling when assessing the outcome of TAR.
Article
Functional imaging experimental designs measuring fatigue, defined as a subjective lack of physical and/or mental energy characterizing a wide range of neurologic conditions, are still under development. Nineteen right‐handed healthy subjects (9 M and 10 F, mean age 43.15 ± 8.34 years) were evaluated by means of functional magnetic resonance imaging (fMRI), asking them to perform explicit, first‐person, mental imagery of fatigue‐related multisensory sensations. Short sentences designed to assess the principal manifestations of fatigue from the Multidimensional Fatigue Symptom Inventory were presented. Participants were asked to imagine the corresponding sensations (Sensory Imagery, SI). As a control, they had to imagine the visual scenes (Visual Imagery, VI) described in short phrases. The SI task (vs. VI task) differentially activated three areas: (i) the precuneus, which is involved in first‐person perspective taking; (ii) the left superior temporal sulcus, which is a multisensory integration area; and (iii) the left inferior frontal gyrus, known to be involved in mental imagery network. The SI fMRI task can be used to measure processing involved in mental imagery of fatigue‐related multisensory sensations. Nineteen right‐handed healthy subjects performed explicit, first person, mental imagery of fatigue‐related multisensory sensations (and imagined visual scenes as control). Sensory Imagery (vs. visual imagery) differentially activated the precuneus, which is involved in first‐person perspective taking, the left superior temporal sulcus, which is a multisensory integration area; and the left inferior frontal gyrus, known to be involved in the mental imagery network. The Sensory Imagery fMRI task is an easily administrable fMRI task, which can be used to measure processing involved in mental imagery of fatigue‐related multisensory sensations.
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Introduction: Faith and systems of beliefs are known to impact not only the emotional, but also the immunological state of believers in ways that we are just starting to understand. Moreover, clinical implications of previous studies are limited. The aim of the “HEALING” (Hospital-based Ecumenical and Linguistic Immuno-NeuroloGic) Study was to examine immunological and neurological changes in hospitalized patients after meeting a chaplain coupled with biblical readings. Methods: Hospitalized patients were pre-screened to find those who were the most in need of an intervention. A passage from the Bible was read to them during a meeting with the chaplain at the bedside (n= 20) or in the chapel (n= 18). No meeting occurred in the randomized control group (n=19). Blood samples were taken 30 minutes prior, and 60 minutes after the meeting to measure white blood cells (WBC), interferon gamma (IFN-γ), immunoglobulin M (IgM), IgA, IgG, and complement 3 (C3). A subgroup of the visited patients was subjected to functional magnetic resonance imaging (fMRI), where they were played an audiotape of readings of the same passage from the Bible (n=21). Results: Lymphocyte counts increased more often after the more successful visits, but the immunological changes were not significant. Conversely, a significant (p fwe =0.003) correlation was revealed between changes in lymphocytes and activation of the angular gyrus (left BA39) during fMRI, a brain area involved in word recognition. Conclusions: Although limited by the sample size and cohort study design, the findings suggest the depth of psycho-immunological changes could depend on the degree to which the chaplains’ main message is understood.
Article
Background Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) are both characterized by cognitive and social impairments. Determining disorder-specific neurobiological alterations in GAD and MDD by means of functional magnetic resonance imaging (fMRI) may promote determination of precise diagnostic markers. Methods This study aimed to examine disorder-specific behavioral and neural alterations at the intersection of social and cognitive processing in treatment-naïve first-episode GAD (n = 35) and MDD (n = 37) patients compared to healthy controls (n = 35) by employing a social-emotional n-back fMRI paradigm. Results No behavioral differences between patients and healthy controls were observed. However, GAD patients exhibited decreased bilateral dorsomedial prefrontal cortex (dmPFC) engagement during the 0-back condition yet increased dmPFC engagement during the 1-back condition compared to MDD and healthy participants. In contrast, MDD patients exhibited increased dmPFC-insula coupling during 0-back, yet decreased coupling during 1-back, compared to GAD and healthy participants. Dimensional symptom-load analysis confirmed that increased dmPFC-insula connectivity during 0-back was positively associated with depressive symptom load. Limitations The moderate sample size in the present study did not allow us to further explore gender differences. In addition, some patients exhibited GAD and MDD comorbidity according to the M.I.N.I. interview. Finally, the paradigm we used did not allow to further examine the specific emotional impacts to working memory. Conclusions These findings suggest that the dmPFC engaged in integrating of affective and cognitive components and self-other processing exhibits GAD-specific neurofunctional dysregulations whereas functional dmPFC communication with insula, a region involved in salience processing, may represent an MDD-specific neurofunctional deficit.
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Biomechanical trajectories generally embody amplitude and temporal effects, but these effects are generally analyzed separately. Here we demonstrate how amplitude-phase separation techniques from the statistics literature can be used to simultaneously analyze both. The approach hinges on nonlinear registration, which temporally warps trajectories to minimize timing effects, and the resulting optimal time warps can be combined with the resulting amplitudes in a simultaneous test. We first analyzed two simulated datasets with controlled amplitude and temporal effects to demonstrate how amplitude-timing separation can avoid incorrect conclusions from common amplitude-only hypothesis testing. We then analyzed two experimental datasets, demonstrating how amplitude-phase separation can yield unique perspectives on the relative contributions of amplitude and timing effects embodied in biomechanical trajectories. Last, we show that the proposed approach can be sensitive to procedural and parameter specifics, so we recommend that these sensitivities should be explored and reported.
Article
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Introduction: Faith and systems of beliefs are known to impact not only the emotional, but also the immunological state of believers in ways that we are just starting to understand. Moreover, clinical implications of previous studies are limited. The aim of the “HEALING” (Hospital-based Ecumenical and Linguistic Immuno-NeuroloGic) Study was to examine immunological and neurological changes in hospitalized patients after meeting a chaplain coupled with biblical readings. Methods: Hospitalized patients were pre-screened to find those who were the most in need of an intervention. A passage from the Bible was read to them during a meeting with the chaplain at the bedside (n= 20) or in the chapel (n= 18). No meeting occurred in the randomized control group (n=19). Blood samples were taken 30 minutes prior, and 60 minutes after the meeting to measure white blood cells (WBC), interferon gamma (IFN-γ), immunoglobulin M (IgM), IgA, IgG, and complement 3 (C3). A subgroup of the visited patients was subjected to functional magnetic resonance imaging (fMRI), where they were played an audiotape of readings of the same passage from the Bible (n=21). Results: Lymphocyte counts increased more often after the more successful visits, but the immunological changes were not significant. Conversely, a significant (p fwe =0.003) correlation was revealed between changes in lymphocytes and activation of the angular gyrus (left BA39) during fMRI, a brain area involved in word recognition. Conclusions: Although limited by the sample size and cohort study design, the findings suggest the depth of psycho-immunological changes could depend on the degree to which the chaplains’ main message is understood.
Article
Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly performed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means performance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occupancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised. Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subsequent clustering step. We assess the use of deep autoencoders for dimensionality reduction prior to applying k-means clustering to the encoded data. We compare this deep clustering method to dimensionality reduction using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clustering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of dimensionality reduction method has a significant effect on group-level measurements of state temporal properties.
Article
Significance Humans are exquisitely sensitive to the spatial arrangement of visual features in objects and scenes, but not in visual textures. Category-selective regions in the visual cortex are widely believed to underlie object perception, suggesting such regions should distinguish natural images of objects from synthesized images containing similar visual features in scrambled arrangements. Contrarily, we demonstrate that representations in category-selective cortex do not discriminate natural images from feature-matched scrambles but can discriminate images of different categories, suggesting a texture-like encoding. We find similar insensitivity to feature arrangement in Imagenet-trained deep convolutional neural networks. This suggests the need to reconceptualize the role of category-selective cortex as representing a basis set of complex texture-like features, useful for a myriad of behaviors.
Chapter
The spinal cord is a vital conduit for the flow of information to and from the brain. Spinal cord injury leads to extensive reorganization in the brain, which influences both recovery and the development of maladaptive changes. Advanced magnetic resonance imaging (MRI) techniques have tremendous potential to assess this change non-invasively at a structural and functional level. Diffusion tensor imaging and brain morphometric techniques elegantly demonstrate structural changes such as volume loss as well as a disruption in axonal integrity. Cortical remapping of functional representation has been well studied on task-based functional MRI (fMRI), while subtle changes in neuronal network connectivity are observed in resting state fMRI studies. Although these changes are seen predominantly in the denervated sensorimotor regions and associated tracts, they affect adjacent and remote regions of the brain as well. Changes in structure and function progress with time and are modified by rehabilitative interventions. MRI offers a better understanding of these changes and may serve as a useful biomarker to predict functional recovery and guide effective therapy in spinal cord injury patients.
Article
Background Unilateral upper limb multitasking brings essential improvements to stroke rehabilitation and prosthetic control. However, the influence and recognition of multiple tasks are core issues in the Motor Imagery (MI) system. Methods First, we design the unilateral upper limb MI experimental paradigm and acquire asynchronous functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram based on MI (MI-EEG) data. Then, Brain activation areas for each task are statistically analyzed by fMRI data. A novel fMRI-weighted Convolutional Neural Network (CNN) is designed to reassign each channel’s weight based on brain activation areas to improve classification accuracy. Finally, the EEG data is classified by the fMRI-weighted CNN. Results The four classes of MI tasks reflect significant activation in brain motor areas. The average classification accuracy of fMRI-weighted CNN is 47.0%. The visualization results show the similarity between fMRI and EEG activation areas in the same task. Conclusions The distinguishability of multiple tasks and the influence on motor areas of the brain are confirmed by fMRI experiments. The classification accuracy of multitasks is improved according to the fMRI-weighted CNN. This paper provides a reference for further research into asynchronous fMRI-EEG modeling and multitask MI classification.
Article
Purpose In Lewy body diseases (LBD), various symptoms occur depending on the distribution of Lewy body in the brain, and the findings of brain perfusion and dopamine transporter single-photon emission computed tomography (DAT-SPECT) also change accordingly. We aimed to evaluate the correlation between brain perfusion SPECT and quantitative indices calculated from DAT-SPECT in patients with LBD. Procedures We retrospectively enrolled 35 patients with LBD who underwent brain perfusion SPECT with N-isopropyl-p-[ ¹²³ I] iodoamphetamine and DAT-SPECT with ¹²³ I-ioflupane. Mini-mental state examination (MMSE) data were also collected from 19 patients. Quantitative indices (specific binding ratio [SBR], putamen-to-caudate ratio [PCR], and caudate-to-putamen ratio [CPR]) were calculated using DAT-SPECT. These data were analysed by the statistical parametric mapping procedure. Results In patients with LBD, decreased PCR index correlated with hypoperfusion in the brainstem (medulla oblongata and midbrain) (uncorrected p < 0.001, k > 100), while decreased CPR index correlated with hypoperfusion in the right temporoparietal cortex (family-wise error corrected p < 0.05), right precuneus (uncorrected p < 0.001, k > 100), and bilateral temporal cortex (uncorrected p < 0.001, k > 100). However, there was no significant correlation between decreased SBR index and brain perfusion. Additionally, the MMSE score was correlated with hypoperfusion in the left temporoparietal cortex (uncorrected p < 0.001). Conclusions This study suggests that regional changes in striatal ¹²³ I-ioflupane accumulation on DAT-SPECT are related to brain perfusion changes in patients with LBD.
Article
Neural representation has long been thought to follow the modularity hypothesis, which states that each type of information corresponds to a specific brain area. Though supported by many studies, this hypothesis surfers the pitfall of inefficiency for information encoding. To overcome difficulties the modularity representation hypothesis faced, researchers have proposed that information may be distributed represented in a specific brain area. The distributed representation hypothesis along with the multi-variate pattern approaches have made great success in detecting representation patterns in the previous decade. However, this hypothesis implicitly requires that the pattern should be transformed in a consistent way with respect to all of the represented information in the specific brain area. And the accuracy and validity of the prediction have never been thoroughly tested. Here in the present study, we tested this prediction in two open datasets compiling the object recognition. We validated the distributed representation patterns in the lateral occipital complex/ventral temporal gyrus where all six classifiers were capable of predicting the correct category represented. Furthermore, we correlated the classifiers’ decision function values to the bold signals and found that the decision function value of the logistic regression classifier was exclusively correlated with activities of the same brain area in both datasets. These results support the distributed representation hypothesis and suggest that our neural system may be embedded within the algorithm of a specific classifier.
Article
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.
Article
The goal of this study was to determine resting state fMRI (rs-fMRI) effective connectivity (RSEC) capacity, agnostic of epileptogenic events, in distinguishing seizure onset zones (SOZ) from propagation zones (pZ). Consecutive patients (2.1-18.2 years old), with epilepsy and hypothalamic hamartoma, pre-operative rs-fMRI-directed surgery, post-operative imaging, and Engel class I outcomes were collected. Cross-spectral dynamic causal modelling (DCM) was used to estimate RSEC between the ablated rs-fMRI-SOZ to its region of highest connectivity outside the HH, defined as the propagation zone (pZ). Pre-operatively, RSEC from the SOZ and PZ was expected to be positive (excitatory), and pZ to SOZ negative (inhibitory), and post-operatively to be either diminished or non-existent. Sensitivity, accuracy, positive predictive value were determined for node-to-node connections. A Parametric Empirical Bayes (PEB) group analysis on pre-operative data was performed to identify group effects and effects of Engel class outcome and age. Pre-operative RSEC strength was also evaluated for correlation with percent seizure frequency improvement, sex, and region of interest size. Of the SOZ’s RSEC, only 3.6% had no connection of significance to the pZ when patient models were individually reduced. Among remaining, 96% were in expected (excitatory signal found from SOZ→pZ and inhibitory signal found from pZ→SOZ) versus 3.6% reversed polarities. Both pre-operative polarity signals were equivalently as expected, with one false signal direction out of 26 each (3.7% total). Sensitivity of 95%, specificity 73%, accuracy of 88%, negative predictive value 88%, and positive predictive value of 88% in identifying and differentiating the SOZ and pZ. Groupwise PEB analysis confirmed SOZ→pZ EC was excitatory, and pZ→SOZ EC was inhibitory. Patients with better outcomes (Engel Ia vs. Ib) showed stronger inhibitory signal (pZ→SOZ). Age was negatively associated with absolute RSEC bidirectionally but had no relationship with Directionality SOZ identification performance. In an additional hierarchical PEB analysis identifying changes from pre-to-post surgery, SOZ→pZ modulation became less excitatory and pZ→SOZ modulation became less inhibitory. This study demonstrates the accuracy of Directionality to identify the origin of excitatory and inhibitory signal between the surgically confirmed SOZ and the region of hypothesized propagation zone in children with DRE due to a HH. Thus, this method validation study in a homogenous DRE population may have potential in narrowing the SOZ-candidates for epileptogenicity in other DRE populations and utility in other neurological disorders.
Article
Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action. Much of the uncertainty that clouds our understanding of the world is caused by other people's actions and values. So there could be specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. Our results suggest that most, but not all, behavioral adaptations to navigate social environments are reused from generic value representation and updating mechanisms.
Article
When people make inferences about other people's minds, called theory of mind (ToM), a cortical network becomes active. The right temporoparietal junction (TPJ) is one of the most consistently responsive nodes in that network. Here we used a pictorial, reaction-time, ToM task to study brain activity in the TPJ and other cortical areas. Subjects were asked to take the perspective of a cartoon character and judge its knowledge of a visual display in front of it. The right TPJ showed evidence of encoding information about the implied visual knowledge of the cartoon head. When the subject was led to believe that the head could see a visual change take place, activity in the right TPJ significantly reflected that change. When the head could apparently not see the same visual change take place, activity in the right TPJ no longer significantly reflected that change. The subject could see the change in all cases; the critical factor that affected TPJ activity was whether the subject was led to think the cartoon character could see the change. We also found that whether the beliefs attributed to the cartoon head were true or false did not significantly affect activity in the present paradigm. These results suggest that the right TPJ may play a role in modeling the contents of the minds of others, perhaps more than it participates in evaluating the truth or falsity of that content.
Article
Skilled reading is important in daily life. While the understanding of the neurofunctional organization of this uniquely human skill has advanced significantly, it does not take into consideration the common bilingual experiences around the world. To examine the role of early bilingualism on the neural substrates supporting English word processing, we compared brain activity, as well as functional connectivity, in Spanish-English early bilingual adults (N = 25) and English monolingual adults (N = 33) during single-word processing. Activation analysis revealed no significant differences between the two groups. A seed-to-voxel analysis using eight a priori selected seed-regions (placed in regions known to be involved in reading) revealed relatively stronger functional connectivity in bilinguals between two sets of regions: left superior temporal gyrus seed positively with left lingual gyrus and left middle frontal gyrus seed negatively with left anterior cingulate cortex. Together these results suggest that an early Spanish-English bilingual experience does not modulate local brain activity for English word reading. It does, however, have some influence on the functional intercommunication between brain regions during reading, specifically in two regions associated with reading, which are functionally connected to those inside and outside of the reading network. We conclude that brain regions involved in processing English words are not that different in Spanish-English early bilingual adults relative to monolingual adult users of English.
Article
Previous research has suggested that top-down sensory prediction facilitates, and may be necessary for, efficient transmission of information in the brain. Here we related infants’ vocabulary development to the top-down sensory prediction indexed by occipital cortex activation to the unexpected absence of a visual stimulus previously paired with an auditory stimulus. The magnitude of the neural response to the unexpected omission of a visual stimulus was assessed at the age of 6 months with functional near-infrared spectroscopy (fNIRS) and vocabulary scores were obtained using the MacArthur-Bates Communicative Development Inventory (MCDI) when infants reached the age of 12 months and 18 months, respectively. Results indicated significant positive correlations between this predictive neural signal at 6 months and MCDI expressive vocabulary scores at 12 and 18 months. These findings provide additional and robust support for the hypothesis that top-down prediction at the neural level plays a key role in infants’ language development.
Article
Episodic memories are not static but can change on the basis of new experiences, potentially allowing us to make valid predictions in the face of an ever-changing environment. Recent research has identified prediction errors during memory retrieval as a possible trigger for such changes. In this study, we used modified episodic cues to investigate whether different types of mnemonic prediction errors modulate brain activity and subsequent memory performance. Participants encoded episodes that consisted of short toy stories. During a subsequent fMRI session, participants were presented videos showing the original episodes, or slightly modified versions thereof. In modified videos, either the order of two subsequent action steps was changed or an object was exchanged for another. Content modifications recruited parietal, temporo-occipital, and parahippocampal areas reflecting the processing of the new object information. In contrast, structure modifications elicited activation in right dorsal premotor, posterior temporal, and parietal areas, reflecting the processing of new sequence information. In a post-fMRI memory test, the participants' tendency to accept modified episodes as originally encoded increased significantly when they had been presented modified versions already during the fMRI session. After experiencing modifications, especially those of the episodes' structure, the recognition of originally encoded episodes was impaired as well. Our study sheds light onto the neural processing of different types of episodic prediction errors and their influence on subsequent memory recall.
Article
Background Although the coracoclavicular (CC) ligaments are classically reconstructed after acromioclavicular (AC) joint injuries, biomechanical studies over the past decade have indicated the importance of an additional reconstruction of the AC ligaments. To date, no kinematic study has investigated the kinematic differences between these reconstruction strategies. Purpose To evaluate the restoration of shoulder motion after an AC injury using a CC ligament, an AC ligament, or a combined reconstruction technique. Study Design Controlled laboratory study. Methods After creating a Rockwood grade V lesion in 14 cadaveric shoulders, the AC joint injury was treated with either a CC ligament reconstruction using a suspension device, an in situ AC ligament reconstruction using 2 coupled soft tissue anchors, or a combination of these 2 techniques. Joint motions were registered during humerothoracic elevation in the coronal plane and protraction in the intact shoulder in a Rockwood V lesion and after the 3 reconstruction strategies. An optical navigation system measured 3-dimensional rotation in the sternoclavicular and scapulothoracic joints, and both rotation and translation were analyzed in the AC joint. Results In the sternoclavicular joint, the CC and combined reconstruction techniques adequately restored clavicular axial rotation, while the AC reconstruction technique showed a better correction of clavicular elevation. Scapulothoracic joint rotations were best restored by reconstructing the AC ligaments. In the AC joint, the relative tilting position and the lateral rotation of the scapula compared with the clavicle were best restored by the suspension device and combined reconstruction. The AC ligament reconstruction technique demonstrated a better restoration of the relative protracted position and resulted in a better correction of the translation of the scapula relative to the clavicle. Conclusion This study illustrates that there are kinematic differences between AC, CC, or combined ligament reconstruction strategies. Although each technique was able to restore different elements of the joint kinematics, none of the strategies completely restored the shoulder girdle to its preinjured state. Clinical Relevance Humerothoracic movements after Rockwood V lesions are best restored using the CC reconstruction technique, and scapulothoracic movements are best restored using the AC ligament reconstruction technique.
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Although recent studies have shown the importance of control in creative problem solving, the neural mechanisms of control processes engaged in retrieval of weak representations, which is closely linked to creative problem solving, remain unclear. The current study aimed to examine the neural mechanisms associated with retrieval of weak representations using functional magnetic resonance imaging and their potential relationships with creativity task performance. For this purpose, participants performed an experimental task that enabled us to directly compare between retrieval of previously unattended-and-weak representations and attended-and-strong representations. Imaging results indicated that the right anterior dorsolateral prefrontal cortex (aDLPFC) was selectively engaged in retrieval of weak representations. Moreover, the right aDLPFC activations were positively correlated with individuals’ creativity task performance but independent of attention-demanding task performance. We therefore suggest that the right aDLPFC plays a key role in retrieval of weak representations and may support creative problem solving.
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We have used positron tomography (PET) to demonstrate that some parts of the motor system exhibit physiological adaptation during the repeated performance of a simple motor task, but others do not. In contrast to the primary sensori-motor cortex, the cerebellum exhibits a decrease in physiological activation (increases in regional blood flow during performance) with practice. A new application of factorial experimental design to PET activation studies was used to make these measurements in four normal males. This design allowed adaptation to be examined by testing for an interaction between regional cerebral blood flow (rCBF) increases brought about by a motor task and the number of trials (time). These findings are interpreted as the neurophysiological correlates of synaptic changes in the cerebellum associated with motor learning in man.
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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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Performance characteristics of a new design of positron tomograph with automatically retractable septa for brain imaging have been studied. The device, consisting of block BGO detectors (8 x 8 elements per block), has a ring diameter of 76 cm and an axial FOV of 106.5 mm. The in-plane resolution is on average 5.8 mm and 5.0 mm (FWHM) for stationary and wobble sampling, respectively, over the central 18 cm of the transaxial FOV. Its unique feature is the capability of data acquisition both in the 'conventional' 2D mode (with septa) or 3D mode (septa retracted) where coincidences between any of the 16 detector rings are acquired. When scattered events are subtracted, the efficiency for a 20 cm diameter uniform cylinder increases overall by a factor of 4.8 between 2D (septa extended) and 3D modes. For a 20 cm phantom the trues/singles ratio is higher for 3D than for 2D but for a given unscattered trues rate, the randoms rate in 3D is higher. At 380 keV the scatter fraction within a 20 cm cylinder is 10% (septa extended) and 36% (retracted). In spite of the increase in scatter when septa are retracted, the increased efficiency in the 3D mode of acquisition yields distinct advantages, particularly in the many studies where tracer concentration is low and consequently where dead time and random rates are less important.
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The functional anatomy of motor skill acquisition was investigated in six normal human subjects who learned to perform a pursuit rotor task with their dominant right hand during serial positron emission tomography (PET) imaging of relative cerebral blood flow (relCBF). The effect of motor execution, rather than learning, was identified by a comparison of four motor performance scans with two control scans (eye movements only). Motor execution was associated with activation of a distributed network involving cortical, striatonigral, and cerebellar sites. Second, the effect of early motor learning was examined. Performance improved from 17% to 66% mean time on target across the four PET scans obtained during pursuit rotor performance. Across the same scans, significant longitudinal increases of relCBF were located in the left primary motor cortex, the left supplementary motor area, and the left pulvinar thalamus. The results demonstrate that changes of regional cerebral activity associated with early learning of skilled movements occur in sites that are a subset of a more widely distributed network that is active during motor execution.
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We used positron emission tomography to contrast changes in cerebral blood flow associated with willed and routine acts. In the six tasks used, volunteers had to make a series of responses to a sequence of stimuli. For the routine acts, each response was completely specified by the stimulus. For the willed acts, the response was open-ended and therefore volunteers had to make a deliberate choice. Willed acts in the two response modalities studied (speaking a word, or lifting a finger) were associated with increased blood flow in the dorsolateral prefrontal cortex (Brodmann area 46). Willed acts were also associated with decreases in blood flow, but the location of these decreases was modality dependent.
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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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Positron emission tomographic (PET) images of regional cerebral blood flow (rCBF) from 30 normal, resting volunteers aged 30 to 85 years were analysed to identify areas where rCBF fell with age. Images were anatomically normalised, and a pixel-by-pixel linear regression was performed to remove differences in global CBF between subjects. Pixels at which rCBF then showed a significant (p less than 0.01) negative correlation with age were identified. They were displayed as a statistical parametric map (SPM) of correlations. We demonstrate an age-related decrease in adjusted rCBF in the cingulate, parahippocampal, superior temporal, medial frontal, and posterior parietal cortices bilaterally, and in the left insular and left posterior prefrontal cortices (omnibus significance, chi 2 = 2,291, p less than 0.0001, df = 1). Decreases in rCBF suggest a regionally specific loss of cerebral function with age. The affected areas were all limbic, or association, cortices. Therefore, these decreases may constitute the cerebral substrate of the cognitive changes that occur during normal aging.
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In order to localize cerebral cognitive or sensorimotor function, activation paradigms are being used in conjunction with PET measures of cerebral activity (e.g., rCBF). The changes in local cerebral activity have two components: a global, region independent change and a local or regional change. As the first step in localizing the regional effects of an activation, global variance must be removed by a normalization procedure. A simple normalization procedure is division of regional values by the whole brain mean. This requires the dependence of local activity on global activity to be one of simple proportionality. This is shown not to be the case. Furthermore, a systematic deviation from a proportional relationship across brain regions is demonstrated. Consequently, any normalization must be approached on a pixel-by-pixel basis by measuring the change in local activity and change in global activity. The changes associated with an activation can be partitioned into global and local effects according to two models: one assumes that the increase in local activity depends on global values and the other assumes independence. It is shown that the increase in activity due to a cognitive activation is independent of global activity. This independence of the (activation) condition effect and the confounding linear effect of global activity on observed local activity meet the requirements for an analysis of covariance, with the "nuisance" variable as global activity and the activation condition as the categorical independent variable. These conclusions are based on analysis of data from 24 scans: six conditions over four normal subjects using a verbal fluency paradigm.(ABSTRACT TRUNCATED AT 250 WORDS)
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The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.
Chapter
Multidimensional scaling is the term used to describe any procedure which starts with the ‘distances’ between a set of points (or individuals or objects), or information about these ‘distances’, and finds a configuration of the points, preferably in a small number of dimensions, usually 2 or 3. By ‘configuration’ we mean a set of co-ordinate values. For example, if we are given the road distances between all pairs of English towns, can we reconstruct a map of England? The map will of course be a two-dimensional configuration.
Article
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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Change-distribution analysis and intersubject averaging of subtracted positron emission tomography (PET) images are new techniques for detecting, localizing, and quantifying state-dependent focal transients in neuronal activity. We previously described their application to cerebral blood flow images (intravenous bolus H215O, Kety autoradiographic model). We now describe their application to images of H215O regional tissue activity without conversion to units of blood flow. The sensitivity and specificity of response detection and the accuracy of response localization were virtually identical for the two types of images. Response magnitude expressed in percent change from rest was slightly, but consistently smaller in tissue-activity images. Response magnitude expressed in z-score was the same for the two-image types. Most research and clinical applications of functional brain mapping can employ images of H215O tissue activity (intravenous bolus, 40-sec nondynamic scan) without conversion to units of blood flow. This eliminates arterial blood sampling, thereby simplifying and minimizing the invasivity of the PET procedure.
Article
This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images.
Article
Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of one or more activated regions of one voxel, or larger, suitably small (e. g., 0.05). We present an approximate analysis giving the probability that one or more activated regions of a specified volume, or larger, could have occurred by chance. These results mean that detecting significant activations no longer depends on a fixed (and high) threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region. The substantial improvement in sensitivity that ensues is illustrated using a power analysis and a simulated phantom activation study. © 1994 Wiley-Liss, Inc.
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Article
Cognitive activation in conjunction with pharmacological challenge was used to demonstrate neuromodulation in man. Using positron emission tomography (PET), measurements of regional cerebral blood flow were made during the performance of memory tasks, before and after the administration of apomorphine (dopamine agonist), buspirone (5-HT1A partial agonist) or placebo. Drug effects on memory-induced increases in regional cerebral blood flow were assessed, on a voxel-by-voxel basis, using statistical parametric mapping. Increases of regional cerebral blood flow in response to the memory challenge were attenuated by apomorphine in the dorsolateral prefrontal cortex and augmented in the retrosplenial region of the posterior cingulate. Conversely, buspirone attenuated blood flow increases in the retrosplenial region. These interactions between drugs and a cognitive challenge can best be interpreted as neuromodulatory effects.
Article
Synopsis Using positron emission tomography (PET) and ¹⁵ Oxygen, regional cerebral blood flow (rCBF) was measured in 33 patients with primary depression, 10 of whom had an associated severe cognitive impairment, and 23 age-matched controls. PET scans from these groups were analysed on a pixel-by-pixel basis and significant differences between the groups were identified on Statistical Parametric Maps (SPMs). In the depressed group as a whole rCBF was decreased in the left anterior cingulate and the left dorsolateral prefrontal cortex ( P < 0·05 Bonferroni-corrected for multiple comparisons). Comparing patients with and without depression-related cognitive impairment, in the impaired group there were significant decreases in rCBF in the left medial frontal gyrus and increased rCBF in the cerebellar vermis ( P < 0·05 Bonferroni-corrected). Therefore an anatomical dissociation has been described between the rCBF profiles associated with depressed mood and depression-related cognitive impairment. The pre-frontal and limbic areas identified in this study constitute a distributed anatomical network that may be functionally abnormal in major depressive disorder.
Article
Repeated measurements of regional cerebral blood flow (rCBF) were made in normal volunteers before, and after, the administration of the 5-HT1A partial agonist, buspirone, or placebo. The difference in rCBF, before and after drug, (buspirone versus placebo) was used to identify brain areas affected by buspirone. Buspirone-induced changes in rCBF were studied under two behavioural conditions (5 word-list learning and 15 word-list learning). Compared to placebo, buspirone increased blood flow in the cuneus during both behavioural states. However, decreases in blood flow, centred in the left dorso-lateral prefrontal cortex and posterior cingulate cortex, were only observed under one of the two behavioural conditions. It is concluded that buspirone-induced alterations in regional cerebral blood flow are better understood, not in relation to the known distribution of monoamine neurotransmitter systems (particularly ascending 5-HT projections), but rather in relation to putative neuronal circuits possibly many synapses "downstream" of buspirone's pharmacological site of action.
Article
Regional cerebral blood flow (rCBF) was measured in 30 schizophrenic patients with severe, persistent and stable symptoms using positron emission tomography (PET). Directed and non-directed correlational analysis of the relationship between psychopathology and rCBF was used to identify brain structures implicated in three behavioural subsyndromes of schizophrenia. Psychopathology and neurophysiology (rCBF) exhibited high correlations in the left medial temporal region, mesencephalic, thalamic and left striatal structures. The highest correlations was in the left parahippocampal region. A canonical analysis of the same data highlighted the left parahippocampal region and left striatum (globus pallidus) as sites which linked the behavioural subsyndromes in terms of shared rCBF correlates. Increasing seventy of psychopathology was associated with increased rCBF in these regions. Dismhibition of left medial temporal lobe activity mediated by fronto-limbic connections is a possible explanation for these findings; however, the prefrontal component appears to be critically dependent on the behavioural subsyndrome.
Article
Anatomical and physiological studies have shown that there is an area specialized for the processing of colour (area V4) in the prestriate cortex of macaque monkey brain. Earlier this century, suggestive clinical evidence for a colour centre in the brain of man was dismissed because of the association of other visual defects with the defects in colour vision. However, since the demonstration of functional specialization in the macaque cortex, the question of a colour centre in man has been reinvestigated, based on patients with similar lesions in the visual cortex. In order to study the colour centre in normal human subjects, we used the technique of positron emission tomography (PET), which measures increases in blood flow resulting from increased activity in the cerebral cortex. A comparison of the results of PET scans of subjects viewing multi-coloured and black-and-white displays has identified a region of normal human cerebral cortex specialized for colour vision.
Article
Intersubject averaging and change-distribution analysis of subtracted positron emission tomographic (PET) images were developed and tested. The purpose of these techniques is to increase the sensitivity and objectivity of functional mapping of the human brain with PET. To permit image averaging, all primary tomographic images were converted to anatomically standardized three-dimensional images using stereotactic anatomical localization and interslice interpolation. Image noise, measured in control-minus-control subtractions, was strongly suppressed by averaging. Signal-to-noise ratio, measured in stimulus-minus-control subtractions (hand vibration minus eyes-closed rest), rose steadily with averaging, confirming the accuracy of our method of anatomical standardization. Distribution analysis of CBF change images (outlier detection by gamma-2 statistic) was assessed as an omnibus test for state-dependent changes in regional neuronal activity. Sensitivity in detecting the somatosensory response rose steadily with averaging, increasing from 50% in individual images to 100% when three or more images were averaged. Specificity was 100% at all averaging levels. Although described here as a technique for functional brain mapping with H2(15O) CBF images, image averaging, and change-distribution analysis are more generally applicable techniques, not limited to a single purpose or tracer.
Article
A fully 3-D reconstruction algorithm has been developed to reconstruct data from a 16 ring PET camera (a Siemens/CTI 953B) with automatically retractable septa. The tomograph is able to acquire coincidences between any pair of detector rings and septa retraction increases the total system count rate by a factor of 7.8 (including scatter) and 4.7 (scatter subtracted) for a uniform, 20 cm diameter cylinder. The reconstruction algorithm is based on 3-D filtered backprojection, expressed in a form suitable for the multi-angle sinogram data. Sinograms which are not measured due to the truncated cylindrical geometry of the tomograph, but which are required for a spatially invariant response function, are obtained by forward projection. After filtering, the complete set of sinograms is backprojected into a 3-D volume of 128x128x31 voxels using a voxel-driven procedure. The algorithm has been validated with simulation, and tested with both phantom and clinical data from the 953B.
A linear spatial correlation model with applications to positron emission tomog-raphy A three-dimensional statistical analysis for rCBF activation studies in human brain
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Physical performance of a positron tomograph for brain imaging with retractable septa
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