fMRI Signal Research Articles

A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

Optimized microburst VNS elicits fMRI responses beyond thalamic-specific response from standard VNS.

In parallel to standard vagus nerve stimulation (VNS), microburst stimulation delivery has been developed. We evaluated the fMRI-related signal changes associated with standard and optimized microburst stimulation in a proof-of-concept study (NCT03446664). Twenty-nine drug-resistant epilepsy patients were prospectively implanted with VNS. Three 3T fMRI scans were collected 2 weeks postimplantation. The maximum tolerated VNS intensity was determined prior to each scan starting at 0.125 mA with 0.125 mA increments. FMRI scans were block-design with alternating 30 sec stimulation [ON] and 30 sec no stimulation [OFF]: Scan 1 utilized standard VNS and Scan 3 optimized microburst parameters to determine target settings. Semi-automated on-site fMRI data processing utilized ON-OFF block modeling to determine VNS-related fMRI activation per stimulation setting. Anatomical thalamic mask was used to derive highest mean thalamic t-value for determination of microburst stimulation parameters. Paired t-tests corrected at P < 0.05 examined differences in fMRI responses to each stimulation type. Standard and microburst stimulation intensities at Scans 1 and 3 were similar (P = 0.16). Thalamic fMRI responses were obtained in 28 participants (19 with focal; 9 with generalized seizures). Group activation maps showed standard VNS elicited thalamic activation while optimized microburst VNS showed widespread activation patterns including thalamus. Comparison of stimulation types revealed significantly greater cerebellar, midbrain, and parietal fMRI signal changes in microburst compared to standard VNS. These differences were not associated with seizure responses. While standard and optimized microburst VNS elicited thalamic activation, microburst also engaged other brain regions. Relationship between these fMRI activation patterns and clinical response warrants further investigation. The study was registered with clinicaltrials.gov (NCT03446664).

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.

Functional magnetic resonance imaging signal has sub-second temporal accuracy

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI) signals is confounded by local cerebral vascular reactivity (CVR), which varies across brain locations. Thus, detecting neuronal synchrony as well as inferring inter-regional causal modulation using fMRI signals can be biased. Here we used fast fMRI measurements sampled at 10 Hz to measure the fMRI latency difference between visual and sensorimotor areas when participants engaged in a visuomotor task. The regional fMRI timing was calibrated by subtracting the CVR latency measured by a breath-holding task. After CVR calibration, the fMRI signal at the lateral geniculate nucleus (LGN) preceded that at the visual cortex by 496 ms, followed by the fMRI signal at the sensorimotor cortex with a latency of 464 ms. Sequential LGN, visual, and sensorimotor cortex activations were found in each participant after the CVR calibration. These inter-regional fMRI timing differences across and within participants were more closely related to the reaction time after the CVR calibration. Our results suggested the feasibility of mapping brain activity using fMRI with accuracy in hundreds of milliseconds.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Intra-V1 functional networks and classification of observed stimuli.

Previous studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity. Here, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation. All inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology. Our findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization.

Systematic cross-sectional age-associations in global fMRI signal topography

Abstract The global signal (GS) in resting-state functional MRI (fMRI), known to contain artifacts and non-neuronal physiological signals, also contains important neural information related to individual state and trait characteristics. Here, we show distinct linear and curvilinear relationships between GS topography and age in a cross-sectional sample of individuals (6-85 years old) representing a significant portion of the lifespan. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across age. The thalamus has stronger contributions to the GS in older-age individuals compared with younger-aged individuals, while the putamen has stronger contributions in younger individuals compared with older individuals. The subcortical nucleus basalis of Meynert shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where contributions of the GS are stronger at early and old age, and weaker in middle age. This differentiation between subcortical and cortical brain activity across age supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on age across the lifespan of human development. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.

Distinct neurochemical influences on fMRI response polarity in the striatum

The striatum, known as the input nucleus of the basal ganglia, is extensively studied for its diverse behavioral roles. However, the relationship between its neuronal and vascular activity, vital for interpreting functional magnetic resonance imaging (fMRI) signals, has not received comprehensive examination within the striatum. Here, we demonstrate that optogenetic stimulation of dorsal striatal neurons or their afferents from various cortical and subcortical regions induces negative striatal fMRI responses in rats, manifesting as vasoconstriction. These responses occur even with heightened striatal neuronal activity, confirmed by electrophysiology and fiber-photometry. In parallel, midbrain dopaminergic neuron optogenetic modulation, coupled with electrochemical measurements, establishes a link between striatal vasodilation and dopamine release. Intriguingly, in vivo intra-striatal pharmacological manipulations during optogenetic stimulation highlight a critical role of opioidergic signaling in generating striatal vasoconstriction. This observation is substantiated by detecting striatal vasoconstriction in brain slices after synthetic opioid application. In humans, manipulations aimed at increasing striatal neuronal activity likewise elicit negative striatal fMRI responses. Our results emphasize the necessity of considering vasoactive neurotransmission alongside neuronal activity when interpreting fMRI signal.

Entorhinal-based path integration selectively predicts midlife risk of Alzheimer's disease.

Entorhinal cortex (EC) is the first cortical region to exhibit neurodegeneration in Alzheimer's disease (AD), associated with EC grid cell dysfunction. Given the role of grid cells in path integration (PI)-based spatial behaviors, we predicted that PI impairment would represent the first behavioral change in adults at risk of AD. We compared immersive virtual reality (VR) PI ability to other cognitive domains in 100 asymptomatic midlife adults stratified by hereditary and physiological AD risk factors. In some participants, behavioral data were compared to 7T magnetic resonance imaging (MRI) measures of brain structure and function. Midlife PI impairments predicted both hereditary and physiological AD risk, with no corresponding multi-risk impairment in episodic memory or other spatial behaviors. Impairments associated with altered functional MRI signal in the posterior-medial EC. Altered PI may represent the transition point from at-risk state to disease manifestation in AD, prior to impairment in other cognitive domains.

Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain.

There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales. Here, electrocorticography (ECoG) measures were used to predict functional MRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. The specificity of this relationship to spatial co-localisation of the two signals was also investigated. We acquired simultaneous ECoG-fMRI in the sensorimotor cortex of three patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was co-localised with fMRI measures across all subjects. The best model of fMRI changes across states was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than any others that were based either on the classical frequency bands or on summary measures of cross-spectral changes. The region-specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

Optimizing the measurement of sample entropy in resting-state fMRI data.

The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.

Impaired value-based decision-making in Parkinson's disease apathy.

Apathy is a common and disabling complication of Parkinson's disease characterized by reduced goal-directed behaviour. Several studies have reported dysfunction within prefrontal cortical regions and projections from brainstem nuclei whose neuromodulators include dopamine, serotonin and noradrenaline. Work in animal and human neuroscience have confirmed contributions of these neuromodulators on aspects of motivated decision-making. Specifically, these neuromodulators have overlapping contributions to encoding the value of decisions, and influence whether to explore alternative courses of action or persist in an existing strategy to achieve a rewarding goal. Building upon this work, we hypothesized that apathy in Parkinson's disease should be associated with an impairment in value-based learning. Using a four-armed restless bandit reinforcement learning task, we studied decision-making in 75 volunteers; 53 patients with Parkinson's disease, with and without clinical apathy, and 22 age-matched healthy control subjects. Patients with apathy exhibited impaired ability to choose the highest value bandit. Task performance predicted an individual patient's apathy severity measured using the Lille Apathy Rating Scale (R = -0.46, P < 0.001). Computational modelling of the patient's choices confirmed the apathy group made decisions that were indifferent to the learnt value of the options, consistent with previous reports of reward insensitivity. Further analysis demonstrated a shift away from exploiting the highest value option and a reduction in perseveration, which also correlated with apathy scores (R = -0.5, P < 0.001). We went on to acquire functional MRI in 59 volunteers; a group of 19 patients with and 20 without apathy and 20 age-matched controls performing the Restless Bandit Task. Analysis of the functional MRI signal at the point of reward feedback confirmed diminished signal within ventromedial prefrontal cortex in Parkinson's disease, which was more marked in apathy, but not predictive of their individual apathy severity. Using a model-based categorization of choice type, decisions to explore lower value bandits in the apathy group activated prefrontal cortex to a similar degree to the age-matched controls. In contrast, Parkinson's patients without apathy demonstrated significantly increased activation across a distributed thalamo-cortical network. Enhanced activity in the thalamus predicted individual apathy severity across both patient groups and exhibited functional connectivity with dorsal anterior cingulate cortex and anterior insula. Given that task performance in patients without apathy was no different to the age-matched control subjects, we interpret the recruitment of this network as a possible compensatory mechanism, which compensates against symptomatic manifestation of apathy in Parkinson's disease.

An fMRI Dataset on Social Reward Processing and Decision Making in Younger and Older Adults

Behavioural and neuroimaging research has shown that older adults are less sensitive to financial losses compared to younger adults. Yet relatively less is known about age-related differences in social decisions and social reward processing. As part of a pilot study, we collected behavioural and functional magnetic resonance imaging (fMRI) data from 50 participants (Younger: N = 26, ages 18–34 years; Older: N = 24, ages 63–80 years) who completed three tasks in the scanner: an economic trust game as the investor with three partners (computer, stranger, friend) as the investee; a card-guessing task with monetary gains and losses shared with three partners (computer, stranger, friend); and an ultimatum game as responder to three anonymous proposers (computer, age-similar adults, age-dissimilar adults). We also collected B0 field maps and high-resolution structural images (T1-weighted and T2-weighted images). These data could be reused to answer questions about moment-to-moment variability in fMRI signal, representational similarity between tasks, and brain structure.

Classification of fMRI Signals Using Brain Activation-Based Decision Tree

Classification of fMRI Signals Using Brain Activation-Based Decision Tree

Local gradient analysis of human brain function using the Vogt-Bailey Index

In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the ‘heavier’ the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.

Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation.

Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat-evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n = 24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the healthy comparison (HC) group (n = 24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than theHC groupduring saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level-dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing in prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.

The missing third dimension-Functional correlations of BOLD signals incorporating white matter.

Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.

The neural origin for asymmetric coding of surface color in the primate visual cortex

The coding privilege of end-spectral hues (red and blue) in the early visual cortex has been reported in primates. However, the origin of such bias remains unclear. Here, we provide a complete picture of the end-spectral bias in visual system by measuring fMRI signals and spiking activities in macaques. The correlated end-spectral biases between the LGN and V1 suggest a subcortical source for asymmetric coding. Along the ventral pathway from V1 to V4, red bias against green peaked in V1 and then declined, whereas blue bias against yellow showed an increasing trend. The feedforward and recurrent modifications of end-spectral bias were further revealed by dynamic causal modeling analysis. Moreover, we found that the strongest end-spectral bias in V1 was in layer 4Cβ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}. Our results suggest that end-spectral bias already exists in the LGN and is transmitted to V1 mainly through the parvocellular pathway, then embellished by cortical processing.

Aberrant interhemispheric structural and functional connectivity within whole brain in schizophrenia

ObjectiveSchizophrenia is a serious mental disorder whose etiology remains unclear. Although numerous studies have analyzed the abnormal gray matter functional activity and whole-brain anatomical changes in schizophrenia, fMRI signal fluctuations from white matter have usually been ignored and rarely reported in the literature. MethodsWe employed 45 schizophrenia subjects and 75 healthy controls (HCs) from a publicly available fMRI dataset. By combining the voxel-mirrored homotopic connectivity (VMHC) measure and fiber tracking method, we investigated the interhemispheric functional and structural connectivity within whole brain in schizophrenia. ResultsCompared to HCs, patients with schizophrenia exhibited significantly reduced VMHC in the bilateral middle occipital gyrus, precentral gyrus, postcentral gyrus and corpus callosum. Fiber tracking results showed the changes in structural connectivity for the bilateral precentral gyrus, and the bilateral corpus callosum, and the fiber bundles connecting bilateral precentral gyrus and connecting the bilateral corpus callosum passed through the posterior midbody, isthmus and splenium of mid-sagittal corpus callosum, which closely related to the interhemispheric integration of visual and auditory information. More importantly, we observed a negative correlation between averaged VMHC values in the postcentral gyrus and SAPS scores, and a positive correlation between the fractional anisotropy of fiber bundle connecting the bilateral precentral gyrus and Matrix Reasoning scores in schizophrenia. ConclusionOur findings provide a novel perspective of white matter functional images on understanding abnormal interhemispheric visual and auditory information transfer in schizophrenia.