Article

Statistical Parametric Maps in Functional Imaging: A General Linear Approach

Wiley
Human Brain Mapping
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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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... Within each tissue type, an average parabolic phase tting method was used to reduce artifacts ampli ed in the Laplacian [43], and the second derivatives of the tted phase were taken to calculate conductivity. The conductivity maps of each participant from both sessions were normalised into Montreal Neurological Institute (MNI) space (voxel size 2 mm isotropic) using statistical parametric mapping (SPM) 12 [44]. rs-fMRI: Using SPM 12 [44], and custom software, fMRI images were processed by N.D. (unblinded). ...
... The conductivity maps of each participant from both sessions were normalised into Montreal Neurological Institute (MNI) space (voxel size 2 mm isotropic) using statistical parametric mapping (SPM) 12 [44]. rs-fMRI: Using SPM 12 [44], and custom software, fMRI images were processed by N.D. (unblinded). Images were slice-time and motion corrected, and global signal drifts removed using the detrending method described by Macey and colleagues [45]. ...
... We selected nine components from six major brain networks: the salience, sensorimotor, visual, default mode, cerebellar and executive control networks ( Figure S2). pCASL: Using SPM 12 [44], pCASL data were analysed by N.D. (unblinded). All pCASL sets were realigned, co-registered to each participant's source image, and a mean cerebral blood ow (CBF) map created using the subtraction method from the ASL toolbox [52]. ...
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Background Head impacts, particularly, non-concussive impacts, are common in sport. Yet, their effects on the brain are poorly understood. Here, we investigated the acute effects of non-concussive impacts on brain microstructure, chemistry, and function using magnetic resonance imaging (MRI) and other techniques. Results Fifteen healthy male soccer players completed this randomised, controlled, crossover trial. Participants completed a soccer heading task (‘Heading’; the Intervention) and an equivalent ‘Kicking’ task (the Control); followed by a series of MRI sequences between ~ 60–120 minutes post-tasks. Blood was also sampled, and cognitive function assessed, pre-, post-, 2.5 hours post-, and 24 hours post-tasks. Brain chemistry: Heading increased total N-acetylaspartate (p = 0.012) and total creatine (p = 0.010) levels in the primary motor cortex (but not the dorsolateral prefrontal cortex) as assessed via proton magnetic resonance spectroscopy. Glutamate-glutamine, myoinositol, and total choline levels were not altered in either region. Brain structure: Heading had no effect on diffusion weighted imaging metrics. However, two blood biomarkers expressed in brain microstructures, glial fibrillary acidic protein and neurofilament light, were elevated 24 hours (p = 0.014) and ~ 7-days (p = 0.046) post-Heading (vs. Kicking), respectively. Brain function: Heading decreased tissue conductivity in five brain regions (p’s < 0.001) as assessed via electrical properties tomography. However, no differences were identified in: (1) connectivity within major brain networks as assessed via resting-state functional MRI; (2) cerebral blood flow as assessed via pseudo continuous arterial spin labelling; (3) electroencephalography frequencies; or (4) cognitive (memory) function. Conclusions This study identified chemical, microstructural and functional brain alterations in response to an acute non-concussive soccer heading task. These alterations appear to be subtle, with some only detected in specific regions, and no corresponding functional deficits (e.g., cognitive, adverse symptoms) observed. Nevertheless, our findings emphasise the importance of exercising caution when performing repeated non-concussive head impacts in sport. Trial registration ACTRN12621001355864. Date of registration 7/10/2021. URL https//www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382590&isReview=true
... Similar analyses in fMRI are difficult, as the sampling rate is two orders of magnitude lower (TR = 1 s = 1 Hz in this study) and the time-series comparably smaller. To enable speech tracking with fMRI, we acquired long time-courses by presenting listening blocks of 5 min and derived spatial maps of speech tracking within a voxel-by-voxel General Linear Model (GLM) 16 framework using envelope time courses convolved with the hemodynamic response (HRF). This analysis was applied to single speaker and auditory scene conditions to extract a coefficient of envelope tracking for each voxel. ...
... To avoid concatenating trials, we performed the tracking analyses for the central portion of single trials (4 min duration). Subsequently, the functional data was analyzed-voxel-by-voxel-for the tracking of speech envelopes by making use of the GLM framework 16 . More specifically, we modeled BOLD voxel time courses by y = X β track + ε where y (n x 1) denotes the voxel time course of n TR (or samples), X (n x p) a design matrix of model time courses for p predictors, β track (p x 1) model coefficients and ε (n x 1) the error term). ...
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Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the single speaker and concurrent speakers conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.
... In the past two decades, this linear transform model has proven very useful. Its simplifying assumptions allow for the use of the General Linear Model (GLM), which is employed in mass univariate Statistical Parametric Mapping (SPM, [8]) and, often, as a preliminary step to multivariate approaches such as Multi Voxel Pattern Analysis (MVPA, [9]) and Representational Similarity Analysis (RSA, [10]). ...
... This new scheme is unique in two ways. First, unlike the commonly used GLM-based approach [8], it is model-free and does not rely on the common assumptions of BOLD linearity or HRF uniformity. Secondly, unlike spatially multivariate approaches such as MVPA and RSA [10,46], this scheme treats entire runs, rather than blocks or trials, as its basic experimental unit. ...
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Functional neuroimaging analysis takes noisy multidimensional measurements as input and produces statistical inferences regarding the functional properties of brain regions as output. Such inferences are most commonly model-based, in that they assume a model of how neural activity translates to the measured signal (blood oxygenation level-dependent signal in the case of functional MRI). The use of models increases statistical sensitivity and makes it possible to ask fine-grained theoretical questions. However, this comes at the cost of making theoretical assumptions about the underlying data-generating process. An advantage of model-free approaches is that they can be used in cases where model assumptions are known not to hold. To this end, we introduce a randomization-based, model-free approach to functional neuroimaging. TWISTER randomization makes it possible to infer functional selectivity from correlations between experimental runs. We provide a proof of concept in the form of a visuomotor mapping experiment and discuss the possible strengths and limitations of this new approach in light of our empirical results.
... To analyze the associations of brain activity with pleasure and surprise, we used a general linear model (GLM), which comprises an analysis method that statistically examines how well the observed signal data can be fitted to a design matrix model [64]. We fitted the time-course data of subjective pleasure or IC (for melody/harmony) as the design matrix model to the time course of the aforementioned average power values. ...
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Background/Objectives: Musical pleasure is considered to be induced by prediction errors (surprise), as suggested in neuroimaging studies. However, the role of temporal changes in musical features in reward processing remains unclear. Utilizing the Information Dynamics of Music (IDyOM) model, a statistical model that calculates musical surprise based on prediction errors in melody and harmony, we investigated whether brain activities associated with musical pleasure, particularly in the θ, β, and γ bands, are induced by prediction errors, similar to those observed during monetary rewards. Methods: We used the IDyOM model to calculate the information content (IC) of surprise for melody and harmony in 70 musical pieces across six genres; eight pieces with varying IC values were selected. Electroencephalographic data were recorded during listening to the pieces, continuously evaluating the participants’ subjective pleasure on a 1–4 scale. Time–frequency analysis of electroencephalographic data was conducted, followed by general linear model analysis to fit the power-value time course in each frequency band to the time courses of subjective pleasure and IC for melody and harmony. Results: Significant positive fits were observed in the β and γ bands in the frontal region with both subjective pleasure and IC for melody and harmony. No significant fit was observed in the θ band. Both subjective pleasure and IC are associated with increased β and γ band power in the frontal regions. Conclusions: β and γ oscillatory activities in the frontal regions are strongly associated with musical rewards induced by prediction errors, similar to brain activity observed during monetary rewards.
... A benchmark study was conducted using a comprehensive panel of non-parametric sampling-based methods on a neural mass model of event-related potentials (ERPs) measured in magneto/encephalography (MEG/EEG) recordings (Sengupta et al., 2015. These studies involved custom code for implementing the tested algorithms, which were released as Markov Chain Monte Carlo (MCMC) inference in the DMC reference toolbox Statistical Parametric Mapping (Friston et al. (1994); Penny et al. (2011)). Initial studies focused on gradient-free MCMC methods, including random walk Metropolis, slice-sampling, adaptive MCMC, and population-based MCMC with tempering (Sengupta et al., 2015). ...
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Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magneto/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuroscience research and applications in neuroimaging through automatic DCM.
... Data processing and analyses were completed with AFNI software. 7 Processing was done using the general linear model developed by Friston et al. 8 Main processing steps included (1) rigid body realigned of all frames to the first volume, (2) slice timing correction, (3) spatial smoothing using a Gaussian kernel FFull Width Half Maximum (FWHM) 4.0 mm, (4) removal of lowfrequency signal drift and (4) registration to a high-resolution anatomical T1 image. The processing pipeline is illustrated in detail in Omisade et al. (Figure 2). ...
Article
Background Functional MRI (fMRI) has proven valuable in presurgical planning for people with brain tumors. However, it is underutilized for patients with epilepsy, likely due to less data on its added clinical value in this population. We reviewed clinical fMRI referrals at the QEII Health Sciences Center (Halifax, Nova Scotia) to determine the impact of fMRI on surgical planning for patients with epilepsy. We focused on reasons for fMRI referrals, findings and clinical decisions based on fMRI findings, as well as postoperative cognitive outcomes. Methods We conducted a retrospective chart review of patients who underwent fMRI between June 2015 and March 2021. Results Language lateralization represented the primary indication for fMRI (100%), with 7.7% of patients also referred for motor and sensory mapping. Language dominance on the side of resection was observed in 12.8% of patients; in 20.5%, activation was adjacent to the proposed resection site. In 18% of patients, fMRI provided an indication for further invasive testing due to the risk of significant cognitive morbidity (e.g., anterograde amnesia). Further invasive testing was avoided based on fMRI findings in 69.2% of patients. Cognitive outcomes based on combined neuropsychological findings and fMRI-determined language dominance were variable. Conclusion fMRI in epilepsy was most often required to identify hemispheric language dominance. Although fMRI-determined language dominance was not directly predictive of cognitive outcomes, it helped identify patients at low risk of catastrophic cognitive morbidity and those at high risk who required additional invasive testing.
... In neuroimaging, three different statistical models were created and estimated according to the general linear model (Friston et al. 1994). The GM probability maps and the two nuisance variables, age and total intracranial volume (TIV), were included in these models. ...
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Common genetic variants of FOXP2 may contribute to schizophrenia vulnerability, but controversial results have been reported for this proposal. Here we evaluated the potential impact of the common FOXP2 rs2396753 polymorphism in schizophrenia. It was previously reported to be part of a risk haplotype for this disease and to have significant effects on gray matter concentration in the patients. We undertook the first examination into whether rs2396753 affects the brain expression of FOXP2 and a replication study of earlier neuroimaging findings of the influence of this genetic variant on brain structure. FOXP2 expression levels were measured in postmortem prefrontal cortex samples of 84 male subjects (48 patients and 36 controls) from the CIBERSAM Brain and the Stanley Foundation Array Collections. High-resolution anatomical magnetic resonance imaging was performed on 79 male subjects (61 patients, 18 controls) using optimized voxel-based morphometry. We found differences in FOXP2 expression and brain morphometry depending on the rs2396753, relating low FOXP2 mRNA levels with reduction of gray matter density. We detected an interaction between rs2396753 and the clinical groups, showing that heterozygous patients for this polymorphism have gray matter density decrease and low FOXP2 expression comparing with the heterozygous controls. This study shows the importance of independent replication of neuroimaging genetic studies of FOXP2 as a candidate gene in schizophrenia. Furthermore, our results suggest that the FOXP2 rs2396753 affects mRNA levels, thus providing new knowledge about its significance as a potential susceptibility polymorphism in schizophrenia.
... While incorporating prior knowledge into data-driven methods has advantages in improving fMRI signal analysis, it inevitably inherits some drawbacks similar to model-driven approaches. Typically, temporal prior knowledge is derived from convolving time series related to experimental design paradigms with the HRF, which is built upon the assumption of the HRF [24]. Based on the aforementioned research and considering the individual differences in HRF among subjects, this study proposes a temporal and spatial constrained dictionary learning and sparse representation method that is more suitable for fMRI data. ...
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Task‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.
... Pre-operative axial non-contrast T1-weighted magnetic resonance (MR) sequences with 1 mm slices were co-registered with postoperative axial non-contrast CT scans with 0.625 mm slices using Statistical Parametric Mapping (SPM) 12 49 . Macro-and microelectrodes were automatically localized on the fused image and manually adjusted using LeGUI software ( Supplementary Fig. 1) 50 . ...
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Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies suggest that the claustrum, a small subcortical nucleus, coordinates slow waves. We show that, in contrast to neurons from other brain regions, claustrum neurons in the human brain increase their spiking activity and track slow waves during NREM sleep, suggesting that the claustrum plays a role in coordinating human sleep architecture.
... In this study, we preprocessed the raw dataset using the Statistical Parametric Mapping toolbox version 12 (SPM12, https://www.fil.ion.ucl.ac.uk/spm/software/s pm12/) in MATLAB [58], which is widely used for analyzing neuroimaging data. We acquired the raw scans with a voxel size of 3.4 × 3.4 × 4 mm. ...
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Background Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. Methods The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson's correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM). Results Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson's correlation-selected features at lobes and Brodmann region level (81% for Gyrus), as well as Hemispheres involving different stages. Thus, the results of our study were consistent with previous studies that have revealed some functional abnormalities in several brain regions. We also discovered the involvement of other brain regions which were never sufficiently studied in previous literature, such as the posterior lobe (posterior cerebellum), Pyramis, and Brodmann Area 34. Conclusions We present a unique and comprehensive approach to investigating the neurological basis of schizophrenia in this study. By bridging the gap between neuroimaging and computable analysis, we aim to improve diagnostic accuracy in patients with schizophrenia and identify potential prognostic markers for disease progression.
... Outlier volumes were identified and censored using the Artifact Detection Tools (ART) software in CONN applying the toolbox's default settings (acquisitions with framewise displacement above 0.9 mm or global BOLD signal changes above 5 standard deviations). Data were then spatially normalized into the standard Montreal Neurological Institute (MNI) space (Friston et al., 1994), resliced to 2 mm × 2 mm × 2 mm voxels, and smoothed using a Gaussian kernel with a fullwidth at half-maximum (FWHM) of 6 mm. ...
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Background Reward and threat processes work together to support adaptive learning during development. Adolescence is associated with increasing approach behavior (e.g., novelty-seeking, risk-taking) but often also coincides with emerging internalizing symptoms, which are characterized by heightened avoidance behavior. Peaking engagement of the nucleus accumbens (NAcc) during adolescence, often studied in reward paradigms, may also relate to threat mechanisms of adolescent psychopathology. Methods 47 typically developing adolescents (9.9–22.9 years) completed an aversive learning task during functional magnetic resonance imaging, wherein visual cues were paired with an aversive sound or no sound. Task blocks involved an escapable aversively reinforced stimulus (CS+ r ), the same stimulus without reinforcement (CS+ nr ), or a stimulus that was never reinforced (CS−). Parent-reported internalizing symptoms were measured using Revised Child Anxiety and Depression Scales. Results Functional connectivity between the NAcc and amygdala differentiated the stimuli, such that connectivity increased for the CS+ r ( p = .023) but not for the CS+ nr and CS−. Adolescents with greater internalizing symptoms demonstrated greater positive functional connectivity for the CS− ( p = .041). Conclusions Adolescents show heightened NAcc-amygdala functional connectivity during escape from threat. Higher anxiety and depression symptoms are associated with elevated NAcc-amygdala connectivity during safety, which may reflect poor safety versus threat discrimination.
... To assess the selective activations elicited by different experimental conditions, we applied a general linear model (GLM) analysis [44]. The model predictors corresponded to the experimental conditions (domain: temporal, moral or political; self-location: actual or projected and lexical control). ...
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Mental time travel (MTT), a cornerstone of human cognition, enables individuals to mentally project themselves into their past or future. It was shown that this self-projection may extend beyond the temporal domain to the spatial and social domains. What about higher cognitive domains? Twenty-eight participants underwent functional magnetic resonance imaging (fMRI) while self-projecting to different political, moral and temporal perspectives. For each domain, participants were asked to judge their relationship to various people (politicians, moral figures, personal acquaintances) from their actual or projected self-location. Findings showed slower, less accurate responses during self-projection across all domains. fMRI analysis revealed self-projection elicited brain activity at the precuneus, medial and dorsolateral prefrontal cortex, temporoparietal junction and anterior insula, bilaterally and right lateral temporal cortex. Notably, 23.5% of active voxels responded to all three domains and 27% to two domains, suggesting a shared brain system for self-projection. For ordinality judgement (self-reference), 52.5% of active voxels corresponded to the temporal domain specifically. Self-projection activity overlapped mostly with the frontoparietal control network, followed by the default mode network, while self-reference showed a reversed pattern, demonstrating MTT’s implication in spontaneous brain activity. MTT may thus be regarded as a ‘mental-experiential travel’, with self-projection as a domain-general construct and self-reference related mostly to time. This article is part of the theme issue ‘Elements of episodic memory: lessons from 40 years of research’.
... Neural activity index (NAI) was computed as the ratio of source activity of tone to the omission (NAI = S Omission /S Tone ). A non-linear transformation using the spatial-normalization algorithm (implemented in SPM8; Friston et al., 1994) was employed to transform individual MRIs to the standard MNI brain. The source maps were plotted using the Surf Ice tool (https://www.nitrc. ...
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Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language’s word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker’s linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term ‘duration’ prior. These findings show that experience with a given language can shape a fundamental aspect of human perception – the neural processing of rhythmic sounds – and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.
... To identify the optimal model for explaining participants' data, we compared the 10 candidate models using the Bayesian information criterion (BIC) as an approximation to the model evidence [102]. We performed Bayesian model comparison at the random-effects group level [103], separately for HC and SCZ, as implemented in SPM12 [104]. Thus, we obtained the protected exceedance probability, i.e., the probability that a given model is more likely than any other model, beyond differences due to chance [103], for each of the 10 candidate models (S2 Table). ...
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Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs. We combined psychophysics, Bayesian modeling, and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in response to audiovisual flash-beep sequences in medicated individuals with schizophrenia who exhibited limited psychotic symptoms. Seventeen participants with schizophrenia and 23 healthy controls reported either the number of flashes or the number of beeps of audiovisual sequences that varied in their audiovisual numeric disparity across trials. Both groups balanced sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both also showed comparable weighting of prior information regarding the signals’ causal structure, although the schizophrenia group slightly overweighted prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of independent estimates of the flash and beep counts, followed by estimates that flexibly combine audiovisual inputs. Our results demonstrate that the core neurocomputational mechanisms for audiovisual perceptual and causal inference in number estimation tasks are largely preserved in our limited sample of medicated post-acute individuals with schizophrenia. Future research should explore whether these findings generalize to unmedicated patients with acute psychotic symptoms.
... As we previously did for patients with iLGG [24], the object-naming network for each participant in the sLGG group was achieved in first-level analysis, by modeling the alternating epochs with a simple boxcar reference vector. A general linear model was applied to each voxel for alternating object-naming and baseline conditions; reference waveforms, corresponding to boxcar functions convolved with a hemodynamic response function, modeled the temporal derivatives [29,30]. The realignment procedure used six additional regressors to model the head movement parameters. ...
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Background. Incidentally discovered low-grade gliomas (iLGGs) are very rare and little is still known about their associated functional imaging activation patterns, white-matter status, and plasticity potential. Recent studies shed light on several clinical factors responsible for the good clinical status observed in these patients versus those with their symptomatic counterpart (sLGGs), including small volume. Comparisons were typically carried out by comparing iLGGs with the wider and more heterogeneous sLGG group. In this study, we investigated whether iLGGs affect the brain differently from comparably small sLGGs. Method. Starting from a sample of 13 patients with iLGG, in the current comparative cross-sectional study, we identified a group of patients with sLGGs, primarily matched by lesion volume. We looked for potential differences between the two groups in language-related functional and structural parameters (the fMRI network associated with naming and white-matter fascicles). Results. The t-test did not show significant differences in the fMRI network, but these emerged when performing masking. No significant differences were observed at the white-matter level. Conclusions. Given that small volumes characterized both groups and that demographic variables were comparable, too, we hypothesized that differences between the two groups could be attributed to alternative lesion-related parameters. We discussed these findings from clinical and neurosurgical perspectives.
... To assess the neurophysiological effects of PD, we modelled spectral and connectivity data as a linear combination of explanatory phenotype variables . This method is commonly applied when analysing functional MRI experiments (Friston et al., 1994) and has also been applied to MEG data (Quinn et al., 2024) (Gohil et al., 2022). We used a general linear model: ...
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Parkinson's disease (PD) is a progressive neurodegenerative disorder which causes debilitating symptoms in both the motor and cognitive domains. The neurophysiological markers of PD include 'oscillopathies' such as diffuse neural oscillatory slowing, dysregulated beta band activity, and changes in interhemispheric functional connectivity; however, the relative importance of these markers as determinants of disease status is not clear. In this study, we used resting state magnetoencephalography data (n = 199 participants, 78 PD, 121 controls) from the open OMEGA repository to investigate changes in spectral power and functional networks in PD. Using a Contrast of Parameter Estimates (COPE) approach, we modelled the effects of PD while controlling for population-level confounds (age, sex, brain volume). Permutation testing revealed highly significant increases in theta (p=0.0001) and decreases in gamma band spectral power (p=0.0001). Building on the group contrast results, we investigated the ability of source-resolved MEG data to distinguish PD from healthy controls. Our approach uses a Partial Least Squares (PLS)-based classifier to find linear combinations of MEG features which independently predict PD. We found MEG-based predictions to be highly sensitive and specific, reaching an optimal AUC-ROC of 0.87 +- 0.04 using a model including spectral power features with 4 independent PLS components, compared to 0.68 +- 0.04 when using functional connectivity. Interpretation of the model weights suggests that oscillatory slowing can be separated into independent posterior theta and global diffuse delta components that can robustly identify individual cases of PD with a high degree of accuracy. This suggests MEG can reveal dissociable, complementary neural processes which contribute to PD.
... Voxel-based Morphometry (VBM) is a widely used analytical approach in neuroimaging research that aims to measure differences in the local concentration of brain tissue across multiple brain images and investigate their association with biological and psychometric variables [1,2]. Comparing neuroimaging data is challenging, since the intensity of Magnetic Resonance (MR) images is not standardized, and brain structures differ across individuals. ...
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Voxel-based Morphometry (VBM) has emerged as a powerful approach in neuroimaging research, utilized in over 7,000 studies since the year 2000. Using Magnetic Resonance Imaging (MRI) data, VBM assesses variations in the local density of brain tissue and examines its associations with biological and psychometric variables. Here, we present deepmriprep, a neural network-based pipeline that performs all necessary preprocessing steps for VBM analysis of T1-weighted MR images using deep neural networks. Utilizing the Graphics Processing Unit (GPU), deepmriprep is 37 times faster than CAT12, the leading VBM preprocessing toolbox. The proposed method matches CAT12 in accuracy for tissue segmentation and image registration across more than 100 datasets and shows strong correlations in VBM results. Tissue segmentation maps from deepmriprep have over 95% agreement with ground truth maps, and its non-linear registration, using supervised SYMNet, predicts smooth deformation fields comparable to CAT12. The high processing speed of deepmriprep enables rapid preprocessing of extensive datasets and thereby fosters the application of VBM analysis to large-scale neuroimaging studies and opens the door to real-time applications. Finally, deepmripreps straightforward, modular design enables researchers to easily understand, reuse, and advance the underlying methods, fostering further advancements in neuroimaging research. deepmriprep can be conveniently installed as a Python package and is publicly accessible at https://github.com/wwu-mmll/deepmriprep.
... After preprocessing, steps should be taken towards running general linear models (GLM) to produce statistical maps that highlight where the brain is specialized for the task (Friston et al., 1994). A user must decide whether to smooth their data. ...
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Diffusion-weighted imaging (DWI) is the primary method to investigate macro- and microstructure of neural white matter in vivo. DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the FSuB-Extractor, a flexible open-source software for creating FSuBs. We demonstrate our processing code and the FSuB-Extractor on an openly-available dataset, the Natural Scenes Dataset.
... The voxel masks for each analysis were coregistered to their respective anatomical image to determine gray matter, white matter, and cerebrospinal fluid fractions using SPM12 (Friston et al., 1994). The metabolite levels were tissue-corrected according to the α-correction method (Harris et al., 2015). ...
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Cognitive flexibility represents the capacity to switch among different mental schemes, providing an adaptive advantage to a changing environment. The neural underpinnings of this executive function have been deeply studied in humans through fMRI, showing that the left inferior frontal cortex (IFC) and the left inferior parietal lobule (IPL) are crucial. Here, we investigated the inhibitory–excitatory balance in these regions by means of γ-aminobutyric acid (GABA+) and glutamate + glutamine (Glx), measured with magnetic resonance spectroscopy, during a cognitive flexibility task and its relationship with the performance level and the local task-induced blood oxygenation level-dependent (BOLD) response in 40 young (18–35 years; 26 female) and 40 older (18–35 years; 21 female) human adults. As the IFC and the IPL are richly connected regions, we also examined whole-brain effects associated with their local metabolic activity. Results did not show absolute metabolic modulations associated with flexibility performance, but the performance level was related to the direction of metabolic modulation in the IPL with opposite patterns in young and older individuals. The individual inhibitory–excitatory balance modulation showed an inverse relationship with the local BOLD response in the IPL. Finally, the modulation of inhibitory–excitatory balance in IPL was related to whole-brain effects only in older individuals. These findings show disparities in the metabolic mechanisms underlying cognitive flexibility in young and older adults and their association with the performance level and BOLD response. Such metabolic differences are likely to play a role in executive functioning during aging and specifically in cognitive flexibility.
... Individual task-related activation was evaluated using a general linear model. 26 Each task condition: go trials, stop-Inhibit, SSD (used as a nuisance regressor for each stop-inhibit event), stop-respond, baseline, and the six movement regressors were modeled with a hemodynamic response function (canonical hemodynamic response function ...
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The neural network mediating successful response inhibition mainly includes right hemisphere activation of the pre‐supplementary motor area, inferior frontal gyrus (IFG), subthalamic nucleus (STN), and caudate nucleus. However, the causal role of these regions in the inhibitory network is undefined. Five patients with Parkinson's disease were assessed prior to and after therapeutic thermal ablation of the right STN in two separate functional magnetic resonance imaging (fMRI) sessions while performing a stop‐signal task. Initiation times were faster but motor inhibition with the left hand (contralateral to the lesion) was significantly impaired as evident in prolonged stop‐signal reaction times. Reduced inhibition after right subthalamotomy was associated (during successful inhibition) with the recruitment of basal ganglia regions outside the established inhibitory network. They included the putamen and caudate together with the anterior cingulate cortex and IFG of the left hemisphere. Subsequent network connectivity analysis (with the seed over the nonlesioned left STN) revealed a new inhibitory network after right subthalamotomies. Our results highlight the causal role of the right STN in the neural network for motor inhibition and the possible basal ganglia mechanisms for compensation upon losing a key node of the inhibition network.
... The field has developed powerful techniques for mapping activation contrasts throughout a measured volume, while accounting for temporal and spatial dependencies, and for the multiple testing across locations. One approach relies on Gaussian field theory (Friston et al. 1994;Worsley et al. 1996), another uses nonparametric permutation tests (Nichols & Holmes, 2002; see also article 324. Nonparametric procedures). ...
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Crossvalidation is a method for estimating predictive performance and adjudicating between multiple models. On each of k folds of the process, k-1 of k independent subsets of the data (training set) are used to fit the parameters of each model and the left-out subset (test set) is used to estimate predictive performance. The method is statistically efficient, because training data are reused for testing and performance estimates combined across folds. The method requires no assumptions, provides nearly unbiased (slightly conservative) estimates of predictive performance, and is generally applicable because it amounts to a direct empirical test of each model.
... Model (Friston et al., 1994) was used to estimate the BOLD signal response for each task separately with parameter estimates restricted to the gray matter mask. For each task, one regressor per was not certified by peer review) is the author/funder. ...
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Cognitive impairments associated with crossed aphasia were investigated in a single case study and a review of the literature. A review of literature identifies 4 main cognitive co-morbidities that are significantly associated with crossed aphasia. We present a case of confirmed crossed aphasia with dyslexia and dysgraphia, in which the latter two cannot be fully explained by the current lesion and are probable developmental disorders (dyslexia/dysgraphia). Extensive longitudinal cognitive investigations and a series of advanced imaging techniques (structural and functional) were used to investigate the cognitive and neuroanatomical basis of crossed aphasia and associated impairments in this patient. Using the results from the literature review and the single case study, we suggest that developmental disorders can be an underlying cause of partial right lateralisation shift of language processes, thereby supporting the theory that developmental disorders can be an underlying cause of crossed aphasia. Highlights Central apraxia, dysgraphia, hemi-neglect & acalculia associated with CA Developmental disorders can underlie partial right lateralisation shift Dysfunction of left hemisphere can cause crossed aphasia Clinically, pre-morbid impairments must be investigated in CA cases
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Background Faith and belief systems impact the emotional as well as immunological states of believers in ways that we are just beginning to understand. However, the clinical implications of prior studies are limited. The aim of the HEALING (Hospital-based Ecumenical and Linguistic Immuno-NeuroloGic) study is to examine immunological and neurological changes in hospitalized patients after meeting with a chaplain coupled with the study of biblical readings. Methods Hospitalized patients were pre-screened to identify those who were most in need of a spiritual intervention. A passage from the Bible was read to them during a meeting with the chaplain at bedside (n = 20) or in the chapel (n = 18). No meeting occurred in the randomized control group (n = 19). Blood samples were obtained 30 min prior and 60 min after the meeting to measure white blood cell (WBC) count, 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), during which they listened to an audiotape of readings of the same biblical passage (n = 21). Results Immunological changes were not significant. Conversely, a significant (pfwe = 0.003) correlation was observed between lymphocyte changes and activation of the angular gyrus (left BA39) during fMRI, a brain area involved in word recognition. Conclusions This article contributes to the relevant literature by helping to create a realistic picture of the possibilities of neuroimmune modulation in clinical practice. Compared to healthy volunteers, the extent of short-term neuroimmunomodulation becomes narrower in a clinical setting. Although limited by the sample size and cohort study design, the findings suggest that the depth of psycho-immunological changes could depend on the degree to which the chaplain’s main message is understood.
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Amyotrophic lateral sclerosis (ALS) affects the cerebral cortex layer-dependently, most notably by the foremost targeting of upper motor neurons (UMNs) sited in layer Vb. Previous studies have shown a retained ability of paralysed patients to activate cortical motor networks, even in late-stage ALS. However, it is currently unknown whether such activation reflects a retained capacity to process sensorimotor inputs or if it is a result of actual motor output. Given the distinct function of individual cortical layers, layer-specific functional measurements may provide insight to this question. In this study, using submillimetre resolution laminar fMRI, we assessed the layer-dependent activation associated with attempted (motor) and passive (somatosensory) movements in a locked-in stage ALS patient. We found robust activation in both superficial and deep layers of primary motor cortex (M1). The peak activation in deep layers was localised to layer Vb. These findings demonstrate preserved activity in deep output layers of M1, possibly reflecting a retained ability to engage surviving UMNs despite years of paralysis. Our study underscores the capacity of laminar fMRI to discern subtle cortical activity and elucidates a promising pathway for probing in vivo human ALS pathology with unprecedented resolution.
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A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating – in a highly empirically constrained manner – the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region’s unique intrinsic “connectivity fingerprint” was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain’s intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses.
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Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered resting-state brain function. An increased excitation-inhibition (E/I) ratio is discussed as a potential pathomechanism but in-vivo evidence of disturbed neurotransmission underlying these functional alterations remains scarce. We compared rs-fMRI local activity (LCOR) between ASD (N=405, N=395) and neurotypical controls (N=473, N=474) in two independent cohorts (ABIDE1 and ABIDE2). We then tested how these LCOR alterations co-localize with specific neurotransmitter systems derived from nuclear imaging and compared them with E/I changes induced by GABAergic (midazolam) and glutamatergic medication (ketamine). Across both cohorts, ASD subjects consistently exhibited reduced LCOR, particularly in higher-order default mode network nodes, alongside increases in bilateral temporal regions, the cerebellum, and brainstem. These LCOR alterations negatively co-localized with dopaminergic (D1, D2, DAT), glutamatergic (NMDA, mGluR5), GABAergic (GABAa) and cholinergic neurotransmission (VAChT). The NMDA-antagonist ketamine, but not GABAa-potentiator midazolam, induced LCOR changes which co-localize with D1, NMDA and GABAa receptors, thereby resembling alterations observed in ASD. We find consistent local activity alterations in ASD to be spatially associated with several major neurotransmitter systems. NMDA-antagonist ketamine induced neurochemical changes similar to ASD-related alterations, supporting the notion that pharmacological modulation of the E/I balance in healthy individuals can induce ASD-like functional brain changes. These findings provide novel insights into neurophysiological mechanisms underlying ASD. One Sentence Summary Local activity alterations in ASD co-localize with glutamatergic and GABAergic neurotransmission and were similar to ketamine-induced brain changes.
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Significance: Current systems for diffuse optical tomography (DOT) are unsuitable for clinical applications on acute traumatic brain injury (TBI) patients while in the intensive care unit (ICU). Aim: To develop and test a method for DOT recordings suitable for TBI patients in the ICU. This method is based on measurements and co-registration using 3-D optical scans, and the acquisition of optical data using a custom-made helmet which would enable a multimodal (invasive and non-invasive) neuromonitoring. Approach: Probe displacements compared to electromagnetic digitization co-registrations were assessed. The capacity to isolate and monitor, using functional near-infrared spectroscopy (fNIRS), the optical signal in the intracranial (ICT) and extracranial tissues (ECT) was tested on 23 healthy volunteers. Participants were scanned with a frequency-domain NIRS device (690 and 830 nm) during 5 Valsalva maneuvers (VM) in a simulated ICU environment. Results: The results showed an average error in probe displacement of 5.5 mm, a sufficient capacity to isolate oxyhemoglobin O2Hb (p=6.4E10-6) and total hemoglobin HbT (p=2.8E10-5 27 ) in the ICT from the ECT, and to follow the changes of hemoglobin in the ICT during the VM (O2Hb, p=9.2E10-4; HbT, p=1.0E10-3). Conclusions: The developed approach appears to be suitable for use on TBI patients in the ICU.
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Healthy social life requires relationships in different levels of personal closeness. Based on ethological, sociological, and psychological evidence, social networks have been divided into five layers, gradually increasing in size and decreasing in personal closeness. Is this division also reflected in brain processing of social networks? During functional MRI, 21 participants compared their personal closeness to different individuals. We examined the brain volume showing differential activation for varying layers of closeness and found that a disproportionately large portion of this volume (80%) exhibited preference for individuals closest to participants, while separate brain regions showed preference for all other layers. Moreover, this bipartition reflected cortical preference for different sizes of physical spaces, as well as distinct subsystems of the default mode network. Our results support a division of the neurocognitive processing of social networks into two patterns depending on personal closeness, reflecting the unique role intimately close individuals play in our social lives.
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Alzheimer’s disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer’s disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer’s disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer’s disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer’s disease stages to identify the pathological changes specific to Alzheimer’s disease progression. We showed that brain morphological changes across Alzheimer’s disease stages were significantly associated with individuals’ neurofilament light chain concentration, a potential biomarker for Alzheimer’s disease, highlighting the biological plausibility of our results.
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Significance Auriculotherapy is a technique based on stimulation applied to specific ear points. Its mechanism of active and clinical efficacy remain to be established. This study aims to assess the role that primary somatosensory cortex may play to validate auriculotherapy mechanisms. Aim This study examined whether tactile stimulation at specific auricular points is correlated with distinct cortical activation in the primary somatosensory cortex. Approach Seventeen healthy adults participated in the study. Tactile stimuli were delivered to the thumb, shoulder, and skin master points on the ear using von Frey filaments. Functional near-infrared spectroscopy was used to measure and spatially map cortical responses. Results This study revealed distinct hemodynamic activity patterns in response to ear point stimulation, consistent with the classic homunculus model of somatotopic organization. Ipsilateral stimulation showed specific cortical activations for the thumb and shoulder points, while contralateral stimulation showed less significant activity. Functional near-infrared spectroscopy effectively captured localized cortical responses to ear tactile stimuli, supporting the somatotopic mapping hypothesis. Conclusion These findings enhance the understanding of sensory processing with auricular stimulation and supports the concepts of auricular cartography that underpins some schools of auriculotherapy practice. Future research should explore bilateral cortical mapping and the integration of other neuroimaging techniques.
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Importance Cannabis is increasingly being used to treat medical symptoms, but the effects on brain function in those using cannabis for these symptoms are not known. Objective To test whether 1 year of cannabis use for medical symptoms after obtaining a medical cannabis card was associated with increased brain activation during working memory, reward, and inhibitory control tasks, areas of cognition affected by cannabis. Design, Setting, and Participants This cohort study was conducted from July 2017 to July 2020 among participants from the greater Boston area who were recruited as part of a clinical trial of individuals seeking medical cannabis cards for anxiety, depression, pain, or insomnia symptoms. Participants were aged between 18 and 65 years. Exclusion criteria were daily cannabis use and cannabis use disorder at baseline. Data analysis was conducted from August 2021 to April 2024. Main Outcomes and Measures Outcomes were whole brain functional activation during tasks involving working memory, reward, and inhibitory control at baseline and after 1 year of medical cannabis card ownership. Results Imaging was collected from participants before and 1 year after obtaining medical cannabis cards, with 57 participants at baseline (38 female [66.7%]; 6 [10.5%] Black and 45 [78.9%] White participants; 1 [1.8%] Hispanic participant; median [IQR] age, 34.0 [24.0-51.0] years) and 54 participants at 1 year (37 female [68.5%]; 4 [7.4%] Black and 48 [88.9%] White participants; 1 [1.9%] Hispanic participant, median [IQR] age, 36.5 [25.0-51.0] years). Imaging was also collected in 32 healthy control participants at baseline (22 female [68.8%]; 2 [6.2%] Black and 27 [84.4%] White participants; 3 [9.4%] Hispanic participants; median [IQR] age, 33.0 [24.8-38.2] years). In all groups and at both time points, functional imaging revealed canonical activations of the probed cognitive processes. No statistically significant difference in brain activation between the 2 time points (baseline and 1 year) in those with medical cannabis cards and no associations between changes in cannabis use frequency and brain activation after 1 year were found. Conclusions and Relevance In this cohort study of adults obtaining medical cannabis cards for medical symptoms, no significant association between brain activation in the areas of cognition of working memory, reward, and inhibitory control and 1 year of cannabis use was observed. The results warrant further studies that probe the association of cannabis at higher doses, with greater frequency, in younger age groups, and with larger, more diverse cohorts.
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Arterial pulsation is crucial for promoting fluid circulation and for influencing neuronal activity. Previous studies assessed the pulsatility index based on blood flow velocity pulsatility in relatively large cerebral arteries of human. Here, we introduce a novel method to quantify the volumetric pulsatility of cerebral microvasculature across cortical layers and in white matter (WM), using high-resolution 4D vascular space occupancy (VASO) MRI with simultaneous recording of pulse signals at 7T. Microvascular volumetric pulsatility index (mvPI) and cerebral blood volume (CBV) changes across cardiac cycles are assessed through retrospective sorting of VASO signals into cardiac phases and estimating mean CBV in resting state (CBV0) by arterial spin labeling (ASL) MRI at 7T. Using data from 11 young (28.4+-5.8 years) and 7 older (61.3+-6.2 years) healthy participants, we investigated the aging effect on mvPI and compared microvascular pulsatility with large arterial pulsatility assessed by 4D-flow MRI. We observed the highest mvPI in the cerebrospinal fluid (CSF) on the cortical surface (0.19+-0.06), which decreased towards the cortical layers as well as in larger arteries. In the deep WM, a significantly increased mvPI (p = 0.029) was observed in the older participants compared to younger ones. Additionally, mvPI in deep WM is significantly associated with the velocity pulsatility index (vePI) of large arteries (r = 0.5997, p = 0.0181). We further performed test-retest scans, non-parametric reliability test and simulations to demonstrate the reproducibility and accuracy of our method. To the best of our knowledge, our method offers the first in vivo measurement of microvascular volumetric pulsatility in human brain which has implications for cerebral microvascular health and its relationship research with glymphatic system, aging and neurodegenerative diseases.
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The human brain experiences functional changes through childhood and adolescence, shifting from an organizational framework anchored within sensorimotor and visual regions into one that is balanced through interactions with later-maturing aspects of association cortex. Here, we link this profile of functional reorganization to the development of ventral attention network connectivity across independent datasets. We demonstrate that maturational changes in cortical organization link preferentially to within-network connectivity and heightened degree centrality in the ventral attention network, whereas connectivity within network-linked vertices predicts cognitive ability. This connectivity is associated closely with maturational refinement of cortical organization. Children with low ventral attention network connectivity exhibit adolescent-like topographical profiles, suggesting that attentional systems may be relevant in understanding how brain functions are refined across development. These data suggest a role for attention networks in supporting age-dependent shifts in cortical organization and cognition across childhood and adolescence.
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Neuroinflammation is a key component underlying multiple neurological disorders, yet non-invasive and cost-effective assessment of in vivo neuroinflammatory processes in the central nervous system remains challenging. Diffusion weighted magnetic resonance spectroscopy (dMRS) has shown promise in addressing these challenges by measuring diffusivity properties of different neurometabolites, which can reflect cell-specific morphologies. Prior work has demonstrated dMRS utility in capturing microglial reactivity in the context of lipopolysaccharide (LPS) challenges and serious neurological disorders, detected as changes of microglial neurometabolite diffusivity properties. However, the extent to which such dMRS metrics are capable of detecting subtler and more nuanced levels of neuroinflammation in populations without overt neuropathology is unknown. Here we examined the relationship between intrinsic, gut-derived levels of systemic LPS and dMRS-based apparent diffusion coefficients (ADC) of choline, creatine, and N-acetylaspartate (NAA) in two brain regions: the thalamus and the corona radiata. Higher plasma LPS concentrations were significantly associated with increased ADC of choline and NAA in the thalamic region, with no such relationships observed in the corona radiata for any of the metabolites examined. As such, dMRS may have the sensitivity to measure microglial reactivity across populations with highly variable levels of neuroinflammation, and holds promising potential for widespread applications in both research and clinical settings.
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When ongoing sensory stimulation reaches the brain, the resulting neural activity reverberates in its recurrent neural networks. How this intrinsic dynamic affects sensory responses is not well understood. To separate the immediate effect of the stimulus from the recurrent brain dynamic we used a new vector-autoregressive model with external input (VARX). Applying this analysis to intracranial recordings in humans, we find that the recurrent connectivity observed during rest is unaltered when humans are watching movies. The recurrent dynamic enhances and prolongs the responses of the brain to scene cuts, fixation onsets, and sound envelope. If one fails to account for these external inputs, then spurious connections appear in the “functional connectivity”. The model reproduces the prominent observation that an external stimulus can reduce intrinsic noise. The model also reveals that sensory areas have mostly outward connections, whereas higher-order brain areas have mostly incoming connections. By combining the concepts of “functional connectivity” and “encoding models” we introduce an analytical approach capable of revealing interactions between external stimulation and internal dynamics that are not apparent when analyzing these concepts in isolation.
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Speech perception requires the binding of spatiotemporally disjoint auditory–visual cues. The corresponding brain network-level information processing can be characterized by two complementary mechanisms: functional segregation which refers to the localization of processing in either isolated or distributed modules across the brain, and integration which pertains to cooperation among relevant functional modules. Here, we demonstrate using functional magnetic resonance imaging recordings that subjective perceptual experience of multisensory speech stimuli, real and illusory, are represented in differential states of segregation–integration. We controlled the inter-subject variability of illusory/cross-modal perception parametrically, by introducing temporal lags in the incongruent auditory–visual articulations of speech sounds within the McGurk paradigm. The states of segregation–integration balance were captured using two alternative computational approaches. First, the module responsible for cross-modal binding of sensory signals defined as the perceptual binding network (PBN) was identified using standardized parametric statistical approaches and their temporal correlations with all other brain areas were computed. With increasing illusory perception, the majority of the nodes of PBN showed decreased cooperation with the rest of the brain, reflecting states of high segregation but reduced global integration. Second, using graph theoretic measures, the altered patterns of segregation–integration were cross-validated.
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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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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.
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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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The relationship between activity within the human auditory cortices and the presentation rate of heard words was investigated by measuring changes in regional cerebral blood flow with positron emission tomography. We demonstrate that in the primary auditory cortices and middle regions of the superior temporal gyri there is a linear relationship between the rate of presentation of heard words and blood flow response. In contrast, the blood flow response in an area of the left posterior superior temporal gyrus (Wernicke's area) is primarily dependent on the occurrence of words irrespective of their rate of presentation. The primary auditory cortices are associated with the early processing of complex acoustic signals whereas Wernicke's area is associated with the comprehension of heard words. This study demonstrates for the first time that time dependent sensory signals (heard words) detected in the primary auditory cortices are transformed into a time invariant output which is channelled to a functionally specialised region - Wernicke's area. Wernicke's area is therefore distinguished from other areas of the auditory cortex by direct observation of signal transformation rather than by association with a specific behavioural task.
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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.
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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.
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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.
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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.
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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.
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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.
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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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