Resting Functional Magnetic Resonance Imaging Research Articles

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used imaging techniques to visualize brain function. The use of inter-network covariation (a functional connectome) is a widely used approach to infer links among different brain networks. While whole brain resting fMRI connectomes are widely used, PET data has mostly been analyzed using a few regions of interest. There has been much less work estimating PET spatial networks and almost no work on their connectivity (covariation) in the context of a whole brain data-driven connectome, nor have there been direct comparisons between whole brain PET and fMRI connectomes. Here we present an approach to leverage spatially constrained ICA to compute an estimate of the PET connectome. Results reveal highly modularized connectome patterns that are complementary to that identified from resting fMRI. Similarly, we were able to identify comparable resting networks from a PiB PET scan that can be directly compared to networks in rest fMRI data and results reveal similar, but not identical, network spatial patterns, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. The resulting networks, decomposed into spatial maps and subject expressions (loading parameters) linked to resting fMRI provide a new way to evaluate the complementary information in PET and fMRI and open up new possibilities for biomarker development.Clinical Relevance-This study analyzes the whole-brain PET and fMRI connectomes, capturing the complementary information from both imaging modalities, thereby introducing a new scope for biomarker development.

Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence.

Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.

The Effect of Swallowing Action Observation Therapy on Resting fMRI in Stroke Patients with Dysphagia.

Many stroke victims have severe swallowing problems. Previous neuroimaging studies have found that several brain regions scattered in the frontal, temporal, and parietal lobes, such as Brodmann's areas (BA) 6, 21, and 40, are associated with swallowing function. This study sought to investigate changes in swallowing function and resting-state functional magnetic resonance imaging (rs-fMRI) in stroke patients with dysphagia following action observation treatment. It also sought to detect changes in brain regions associated with swallowing in stroke patients. In this study, 12 healthy controls (HCs) and 12 stroke patients were recruited. Stroke patients were given 4 weeks of action observation therapy. In order to assess the differences in mfALFF values between patients before treatment and HCs, the fractional amplitude of low-frequency fluctuations (fALFF) in three frequency bands (conventional frequency band, slow-4, and slow-5) were calculated for fMRI data. The significant brain regions were selected as regions of interest (ROIs) for subsequent analysis. The mfALFF values were extracted from ROIs of the three groups (patients before and after treatment and HCs) and compared to assess the therapeutic efficacy. In the conventional band, stroke patients before treatment had higher mfALFF in the inferior temporal gyrus and lower mfALFF in the calcarine fissure and surrounding cortex and thalamus compared to HCs. In the slow-4 band, there was no significant difference in related brain regions between stroke patients before treatment and HCs. In the slow-5 band, stroke patients before treatment had higher mfALFF in inferior cerebellum, inferior temporal gyrus, middle frontal gyrus, and lower mfALFF in calcarine fissure and surrounding cortex compared to HCs. We also assessed changes in aberrant brain activity that occurred both before and after action observation therapy. The mfALFF between stroke patients after therapy was closed to HCs in comparison to the patients before treatment. Action observation therapy can affect the excitability of certain brain regions. The changes in brain function brought about by this treatment may help to further understand the potential mechanism of network remodeling of swallowing function.

Abnormal Functional Connectivity Between Cerebral Hemispheres in Patients With High Myopia: A Resting FMRI Study Based on Voxel-Mirrored Homotopic Connectivity.

PurposeTo study the changes in functional connections between the left and right hemispheres of patients with high myopia (HM) and healthy controls (HCs) by resting functional magnetic resonance imaging (fMRI) based on voxel-mirrored homotopic connectivity (VMHC). To study the changes in resting-state functional connectivity (rsFC) between the left and right hemispheres of patients with HM and healthy controls (HCS) at rest by using resting functional magnetic resonance imaging (fMRI) based on voxel-mirror homotopy connectivity (VMHC).Patients and MethodsA total of 89 patients with HM (41 men and 48 women) and 59 HCs (24 men and 35 women) were collected and matched according to gender, age, and education level. The VMHC method was used to evaluate the changes in rsFC between cerebral hemispheres, and a correlation analysis was carried out to understand the differences in brain functional activities between the patients with HM and the HCs.ResultsCompared with the HCs, the VMHC values of the putamen and fusiform in the HM group were significantly lower (voxel-level p < 0.01, Gaussian random field correction cluster level p < 0.05).ConclusionThis study preliminarily confirmed the destruction of interhemispheric functional connection in some brain regions of the patients with HM and provided effective information for clarifying the neural mechanism of patients with HM.

Effect of Alzheimer’s pathology on the functional architecture of memory networks during rest and task states in aging

AbstractBackgroundIntrinsic brain networks are relatively consistently organized between rest and task states in young individuals. We performed this study in order to determine whether the same is true in aging, and whether measures of AD pathology affect this relationship.MethodWe used task‐based (mnemonic discrimination task with object and scene stimuli) and rest fMRI to measure functional connectivity (FC) in an anterior temporal (AT) and posteromedial (PM) network in older (OA; N=49, 74±5 years, 31F) and younger adults (YA; N=24, 27±4 years, 9F). We calculated the correlation between rest and task FC states by conducting a Pearson’s correlation between the rest and task FC matrices for each participant. We used PiB‐PET to measure global Aβ (using threshold of 1.065 to classify OA as PiB+/‐) and FTP‐PET to measure tau within an AT and PM composite ROI in OA. We also measured recognition memory performance during the mnemonic discrimination task requiring discrimination of repeated stimuli from lures.ResultResting state and task FC in the ATPM network were less correlated in older compared to younger adults. In addition, less correlated rest vs. task FC was associated with higher levels of FTP in ATPM networks in PiB+ older adults. Finally, less correlated rest vs. task FC was associated with worse recognition performance (among all participants and OA alone) during the object trials.ConclusionThese findings link AD pathology to disruption of the functional architecture of intrinsic brain networks during rest and memory task states in OA. They also suggest that memory decline in some older adults may be influenced by the extent of change in functional architecture between rest and task demands. Overall, dynamic FC changes from rest to task may provide a novel mechanism in which aging impacts cognition.

Classification of Dynamic Resting fMRI Connectivity of Mood and Mental Disorders Using Different Categorization

Psychosis and mood nosology, based on clinical symptoms, is still controversial and challenging in mental health. Analyzing data-driven extracted features and classification performance on two categorizations based on widely used DSM criteria, and the other, based on additional neuroimaging data collected during the study, can inform mental health diagnosis.

A Classification-Based Approach to Estimate the Number of Resting Functional Magnetic Resonance Imaging Dynamic Functional Connectivity States.

Aim: To determine the optimal number of connectivity states in dynamic functional connectivity analysis. Introduction: Recent work has focused on the study of dynamic (vs. static) brain connectivity in resting functional magnetic resonance imaging data. In this work, we focus on temporal correlation between time courses extracted from coherent networks called functional network connectivity (FNC). Dynamic FNC is most commonly estimated using a sliding window-based approach to capture short periods of FNC change. These data are then clustered to estimate transient connectivity patterns or states. Determining the number of states is a challenging problem. The elbow criterion is one of the widely used approaches to determine the connectivity states. Materials and Methods: In our work, we present an alternative approach that evaluates classification (e.g., healthy controls [HCs] vs. patients) as a measure to select the optimal number of states (clusters). We apply different classification strategies to perform classification between HCs and patients with schizophrenia for different numbers of states (i.e., varying the model order in the clustering algorithm). We compute cross-validated accuracy for different model orders to evaluate the classification performance. Results: Our results are consistent with our earlier work which shows that overall accuracy improves when dynamic connectivity measures are used separately or in combination with static connectivity measures. Results also show that the optimal model order for classification is different from that using the standard k-means model selection method, and that such optimization improves cross-validated accuracy. The optimal model order obtained from the proposed approach also gives significantly improved classification performance over the traditional model selection method. Conclusion: The observed results suggest that if one's goal is to perform classification, using the proposed approach as a criterion for selecting the optimal number of states in dynamic connectivity analysis leads to improved accuracy in hold-out data.

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

Brain functional connectivity in severe asymptomatic carotid stenosis patients on the background of leeching course

Study the influence of the leeching course on brain functional connectivity in 10 patients (8 women and 2 man, average age 69 ± 5.4 years) with asymptomatic atherosclerotic one or both ICA stenosis in the range of 65–75%. Magnetic resonance imaging (MRI), functional MRI at rest fMRI in 2 time points: before and after leeching course, which consisted of 10 sessions. Statistical processing and evaluation of the results of neuroimaging studies of each patient and their group (rest fMRI data) were carried out using the CONN v software package.18, designed to determine the relationships between different parts of the brain, the structure of different rest networks and functional networks of the brain. After a course of treatment (2-2.5 months), was identified the strengthening of positive functional connections of the medial prefrontal cortex with the 10 zone of the cerebellum (right hemisphere), the cerebellum worm. The strengthening of negative functional connections of the medial prefrontal cortex with the left middle frontal gyrus and the right parahyppocampal gyrus was determined. Selecting as the region of interest cerebellar rear of the working of the network is determined by the gain (including local) positive functional connections after hirudotherapy course with right and left hemisphere of the cerebellum (8 zone), the worm of the cerebellum, the cortex and the precuneus, wedge, angular gyrus and posterior divisions of the cingulate gyrus (part of the RSP – the working network of the rest by default).The obtained data are the first, pilot and require further detailed study.

Resting‐state functional magnetic resonance imaging versus task‐based activity for language mapping and correlation with perioperative cortical mapping

IntroductionPreoperative language mapping using functional magnetic resonance imaging (fMRI) aims to identify eloquent areas in the vicinity of surgically resectable brain lesions. fMRI methodology relies on the blood‐oxygen‐level‐dependent (BOLD) analysis to identify brain language areas. Task‐based fMRI studies the BOLD signal increase in brain areas during a language task to identify brain language areas, which requires patients' cooperation, whereas resting‐state fMRI (rsfMRI) allows identification of functional networks without performing any explicit task through the analysis of the synchronicity of spontaneous BOLD signal oscillation between brain areas. The aim of this study was to compare preoperative language mapping using rsfMRI and task fMRI to cortical mapping (CM) during awake craniotomies.MethodsFifty adult patients surgically treated for a brain lesion were enrolled. All patients had a presurgical language mapping with both task fMRI and rsfMRI. Identified language networks were compared to perioperative language mapping using electric cortical stimulation.ResultsResting‐state fMRI was able to detect brain language areas during CM with a sensitivity of 100% compared to 65.6% with task fMRI. However, we were not able to perform a specificity analysis and compare task‐based and rest fMRI with our perioperative setting in the current study. In second‐order analysis, task fMRI imaging included main nodes of the SN and main areas involved in semantics were identified in rsfMRI.ConclusionResting‐state fMRI for presurgical language mapping is easy to implement, allowing the identification of functional brain language network with a greater sensitivity than task‐based fMRI, at the cost of some precautions and a lower specificity. Further study is required to compare both the sensitivity and the specificity of the two methods and to evaluate the clinical value of rsfMRI as an alternative tool for the presurgical identification of brain language areas.

Brain connectivity changes in patients with working memory impairments with chronic ischemic cerebrovascular disease

One of the methods of assessment of cognitive functions in patients with chronic ischemic cerebrovascular disease — CICD (dyscirculatory encephalopathy) implies studying connectivity of neural networks through the analysis of rest functional magnetic resonance imaging (rest fMRI) data. The main objective of this study was to assess the relationship between working memory (WM) characteristics and connectivity of various parts of the brain in patients diagnosed with CICD. The study involved 22 female CICD patients; they were divided into two groups, one with satisfactory level of WM and the other with compromised WM. We assessed intra-brain connectivity with the help of rest fMRI, using the SPM-12 and CONN18b software applications in Matlab platform. The other aspects evaluated were the gray to white matter ratio and the association of this indicator with WM. Significant differences in the intra-brain connectivity were registered in both the satisfactory WM group and the compromised WM group. The brain parts where those differences were found are left parahippocampal area and right supramarginal gyrus; right cerebellar hemisphere and left parietal, as well as left frontal areas; right cingular and left lingual gyri. In addition, we detected significant differences in the ratio in the gray and white matter volumes in both groups (p = 0.007). The results obtained indicate that memory deterioration in CICD patients is concomitant with deteriorating connectivity between the cortical areas, as well as between cerebellum and cortex, which may be associated with a more significant loss of the white matter.

A brain task state only arouses a few number of resting-state intrinsic modes

Brain function in the resting state can be decomposed into independent spontaneous subactivities representing brain intrinsic modes. A brain task-performing activity can also be decomposed into independent modes. Previous research has shown a high spatial correspondence between the resting-state and task-present modes, but how the brain task and resting states are related remains elusive. We propose to establish a framework spanned by universal intrinsic modes (generated from a resting data analysis) to represent a brain task state through task-specific modification on a subset of intrinsic modes. We demonstrate our model and method through experimental data analysis on one group of task fMRI data and another group of rest fMRI data (from 40 different subjects). Our fingertapping experiment results show that the primary motor task only modulates a few intrinsic modes in the functional framework. The brain functional framework (spanned by universal intrinsic modes) offers a unification representation of brain task and resting states for simplifying brain functional organization

Involvement of the Precuneus/Posterior Cingulate Cortex Is Significant for the Development of Alzheimer's Disease: A PET (THK5351, PiB) and Resting fMRI Study.

Background: Imaging studies in Alzheimer’s disease (AD) have yet to answer the underlying questions concerning the relationship among tau retention, neuroinflammation, network disruption and cognitive decline. We compared the spatial retention patterns of 18F-THK5351 and resting state network (RSN) disruption in patients with early AD and healthy controls.Methods: We enrolled 23 11C-Pittsburgh compound B (PiB)-positive patients with early AD and 24 11C-PiB-negative participants as healthy controls. All participants underwent resting state functional MRI and 18F-THK5351 PET scans. We used scaled subprofile modeling/principal component analysis (SSM/PCA) to reduce the complexity of multivariate data and to identify patterns that exhibited the largest statistical effects (variances) in THK5351 concentration in AD and healthy controls.Findings: SSM/PCA identified a significant spatial THK5351 pattern composed by mainly three clusters including precuneus/posterior cingulate cortex (PCC), right and left dorsolateral prefrontal cortex (DLPFC) which accounted for 23.6% of the total subject voxel variance of the data and had 82.6% sensitivity and 79.1% specificity in discriminating AD from healthy controls. There was a significant relationship between the intensity of the 18F-THK5351 covariation pattern and cognitive scores in AD. The spatial patterns of 18F-THK5351 uptake showed significant similarity with intrinsic functional connectivity, especially in the PCC network. Seed-based connectivity analysis from the PCC showed significant decrease in connectivity over widespread brain regions in AD patients. An evaluation of an autopsied AD patient with Braak V showed that 18F-THK5351 retention corresponded to tau deposition, monoamine oxidase-B (MAO-B) and astrogliosis in the precuneus/PCC.Interpretation: We identified an AD-specific spatial pattern of 18F-THK5351 retention in the precuneus/PCC, an important connectivity hub region in the brain. Disruption of the functional connections of this important network hub may play an important role in developing dementia in AD.

Measuring functional connectivity in stroke: Approaches and considerations

Recent research has demonstrated the importance of global changes to the functional organization of brain network following stroke. Resting functional magnetic resonance imaging (R-fMRI) is a non-invasive tool that enables the measurement of functional connectivity (FC) across the entire brain while placing minimal demands on the subject. For these reasons, it is a uniquely appealing tool for studying the distant effects of stroke. However, R-fMRI studies rely on a number of premises that cannot be assumed without careful validation in the context of stroke. Here, we describe strategies to identify and mitigate confounds specific to R-fMRI research in cerebrovascular disease. Five main topics are discussed: (a) achieving adequate co-registration of lesioned brains, (b) identifying and removing hemodynamic lags in resting BOLD, (c) identifying other vascular disruptions that affect the resting BOLD signal, (d) selecting an appropriate control cohort, and (e) acquiring sufficient fMRI data to reliably identify FC changes. For each topic, we provide guidelines for steps to improve the interpretability and reproducibility of FC-stroke research. We include a table of confounds and approaches to identify and mitigate each. Our recommendations extend to any research using R-fMRI to study diseases that might alter cerebrovascular flow and dynamics or brain anatomy.

Sleep duration and resting fMRI functional connectivity: examination of short sleepers with and without perceived daytime dysfunction.

BackgroundApproximately 30% of the U.S. population reports recurrent short sleep; however, perceived sleep need varies widely among individuals. Some “habitual short sleepers” routinely sleep 4–6 hr/night without self‐reported adverse consequences. Identifying neural mechanisms underlying individual differences in perceived sleep‐related dysfunction has important implications for understanding associations between sleep duration and health.MethodThis study utilized data from 839 subjects of the Human Connectome Project to examine resting functional connectivity associations with self‐reported short sleep duration, as well as differences between short sleepers with versus without reported dysfunction. Functional connectivity was analyzed using a parcellation covering the cortical, subcortical, and cerebellar gray matter at 5 mm resolution.ResultsSelf‐reported sleep duration predicts one of the primary patterns of intersubject variance in resting functional connectivity. Compared to conventional sleepers, both short sleeper subtypes exhibited resting fMRI (R‐fMRI) signatures consistent with diminished wakefulness, potentially indicating inaccurate perception of functionality among those denying dysfunction. Short sleepers denying dysfunction exhibited increased connectivity between sensory cortices and bilateral amygdala and hippocampus, suggesting that efficient sleep‐related memory consolidation may partly explain individual differences in perceived daytime dysfunction.ConclusionsOverall, current findings indicate that R‐fMRI investigations should include assessment of average sleep duration during the prior month. Furthermore, short sleeper subtype findings provide a candidate neural mechanism underlying differences in perceived daytime impairment associated with short sleep duration.

Approaches to Capture Variance Differences in Rest fMRI Networks in the Spatial Geometric Features: Application to Schizophrenia.

Identification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networks. Most such techniques involve differentiating groups based on group mean measures. There has been little work focused on differences in spatial characteristics of resting fMRI data. We present a method to identify between group differences in the variability in the cluster characteristics of network regions within components estimated via independent vector analysis (IVA). IVA is a blind source separation approach shown to perform well in capturing individual subject variability within a group model. We evaluate performance of the approach using simulations and then apply to a relatively large schizophrenia data set (82 schizophrenia patients and 89 healthy controls). We postulate, that group differences in the intra-network distributional characteristics of resting state network voxel intensities might indirectly capture important distinctions between the brain function of healthy and clinical populations. Results demonstrate that specific areas of the brain, superior, and middle temporal gyrus that are involved in language and recognition of emotions, show greater component level variance in amplitude weights for schizophrenia patients than healthy controls. Statistically significant correlation between component level spatial variance and component volume was observed in 19 of the 27 non-artifactual components implying an evident relationship between the two parameters. Additionally, the greater spread in the distance of the cluster peak of a component from the centroid in schizophrenia patients compared to healthy controls was observed for seven components. These results indicate that there is hidden potential in exploring variance and possibly higher-order measures in resting state networks to better understand diseases such as schizophrenia. It furthers comprehension of how spatial characteristics can highlight previously unexplored differences between populations such as schizophrenia patients and healthy controls.

Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain.

We have seen important strides in our understanding of mechanisms underlying stroke recovery, yet effective translational links between basic and applied sciences, as well as from big data to individualized therapies, are needed to truly develop a cure for stroke. We present such an approach using The Virtual Brain (TVB), a neuroinformatics platform that uses empirical neuroimaging data to create dynamic models of an individual’s human brain; specifically, we simulate fMRI signals by modeling parameters associated with brain dynamics after stroke.In 20 individuals with stroke and 11 controls, we obtained rest fMRI, T1w, and diffusion tensor imaging (DTI) data. Motor performance was assessed pre-therapy, post-therapy, and 6–12 months post-therapy. Based on individual structural connectomes derived from DTI, the following steps were performed in the TVB platform: (1) optimization of local and global parameters (conduction velocity, global coupling); (2) simulation of BOLD signal using optimized parameter values; (3) validation of simulated time series by comparing frequency, amplitude, and phase of the simulated signal with empirical time series; and (4) multivariate linear regression of model parameters with clinical phenotype. Compared with controls, individuals with stroke demonstrated a consistent reduction in conduction velocity, increased local dynamics, and reduced local inhibitory coupling. A negative relationship between local excitation and motor recovery, and a positive correlation between local dynamics and motor recovery were seen.TVB reveals a disrupted post-stroke system favoring excitation-over-inhibition and local-over-global dynamics, consistent with existing mammal literature on stroke mechanisms. Our results point to the potential of TVB to determine individualized biomarkers of stroke recovery.

ID 381 – Functional neuroimaging study of patients with disorders of consciousness. Value of default mode network (DMN)

Resting FMRI allows estimation the neural networks activation, connectivity in the brain with present or absent evidence of consciousness. Patients with disorders of consciousness (DOC) show selective activation of the default mode network (DMN) as consciousness level increases. The study aim was to evaluate the DMN activation parameters in patients with unresponsive wakefulness syndrome (UWS) and minimal consciousness state (MCS). Material and methods: 4 patients with DOC: 1-anoxic, 1-traumatic, 1-hemorrhagic stroke, 1-acute disseminated encephalomyelitis. CRS-R test score – 2,12,18,18 points respectively, which complies with UWS in the first case and MCS in others. Control – 3 healthy volunteers. All underwent resting FMRI assessing the parameters of DMN. Results: DMN activity was not detected in patient with UWS. However, in MCS, we found a significant activity increase in DMN structures: index of functional connectivity in MCS patients was statistically significant for the right and left parahippocampal gyri (3,46:20,31 in control group) and for the left cingulate gyrus and sulcus intraparietal – 6,28; in control group functional connectivity in these structures is absent. Conclusions: neuronal connectivity of DMN in healthy volunteers significantly differs from MCS and DMN activity is lost in UWS. Connectivity quantity may contribute to impaired consciousness diagnosis.

Neural substrates of motor and non-motor symptoms in Parkinson's disease: a resting FMRI study.

BackgroundRecently, non-motor symptoms of Parkinson’s disease (PD) have been considered crucial factors in determining a patient’s quality of life and have been proposed as the predominant features of the premotor phase. Researchers have investigated the relationship between non-motor symptoms and the motor laterality; however, this relationship remains disputed. This study investigated the neural connectivity correlates of non-motor and motor symptoms of PD with respect to motor laterality.MethodsEight-seven patients with PD were recruited and classified into left-more-affected PD (n = 44) and right-more affected PD (n = 37) based on their MDS-UPDRS (Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale) motor examination scores. The patients underwent MRI scanning, which included resting fMRI. Brain regions were labeled as ipsilateral and contralateral to the more-affected body side. Correlation analysis between the functional connectivity across brain regions and the scores of various symptoms was performed to identify the neural connectivity correlates of each symptom.ResultsThe resting functional connectivity centered on the ipsilateral inferior orbito-frontal area was negatively correlated with the severity of non-motor symptoms, and the connectivity of the contralateral inferior parietal area was positively correlated with the severity of motor symptoms (p < 0.001, |r| > 0.3).ConclusionsThese results suggest that the inferior orbito-frontal area may play a crucial role in non-motor dysfunctions, and that the connectivity information may be utilized as a neuroimaging biomarker for the early diagnosis of PD.