Analysis Of Resting-state fMRI Data Research Articles

Expression of microRNA induced by postoperative delirium-like behavior is associated with long-term default mode network disruption: Sequencing and a secondary analysis of resting-state fMRI data.

Resting state functional magnetic resonance imaging (rs-fMRI) has been widely used in studying default mode network (DMN) changes in postoperative delirium (POD). Reproducibility and interpretability of the analyzing results remain insufficiently studied. Delirium-like behavior was induced by tibial fixation surgery under isoflurane anesthesia. Firstly, we evaluated delirium-like behavior and inflammatory responses in hippocampus and systemic level. Then the expressions of microRNA (miRNA) and target gene were sequenced and validated. Afterwards the functional connectivity (FC) in DMN was analyzed. Finally, results were correlated with DMN changes. POD-like behavior caused significant changes of miR-34b-5p, miR-328-5p, and miR-3505 in miRNA level and Nos1, Tubb3, and Gys1 in the gene level. The FC in left and right hippocampus (L-Hip and R-Hip) and right auditory cortex (R-AC) was found significantly changed. Significant correlations were found in FCL-Hip/R-AC and FCR-Hip/R-AC for miR-34b-5p and miR-3505, as well as Nos1 and Tubb3. For miR-328-5p, no significant correlations were found. Our study demonstrates that POD-like behavior induced significant miRNA and gene expression changes were associated with hippocampus related long-term FC disruption in DMN. The results increased reproducibility and interpretability for standardized rs-fMRI data analysis, as well as providing potential targets for postoperative delirium treatment.

Effects of D-CAG chemotherapy regimen on cognitive function in patients with acute myeloid leukaemia: A resting-state functional magnetic resonance imaging study.

Post-chemotherapy cognitive impairment, also known as 'chemobrain', is a common neurotoxic complication induced by chemotherapy, which has been reported in many cancer survivors who have undergone chemotherapy. In this study, we aimed to explore the effects of D-neneneba dicitabine, C-nenenebb cytarabine, A-aclamycin, G-granulocyte colony-stimulating factor (D-CAG) chemotherapy on cognitive function in patients with acute myeloid leukaemia (AML) and its possible central mechanisms. Twenty patients with AML and 25 matched healthy controls (HC) were enrolled in this study. The cognitive function of patients before and after D-CAG chemotherapy was evaluated by the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog). The resting-state functional magnetic resonance imaging data were collected from all patients before and after chemotherapy intervention, as well as HC. Then, resting-state functional magnetic resonance imaging data were preprocessed using DPABI software package and regional homogeneity (ReHo) values of brain regions were calculated. Finally, ReHo values between groups were compared by Resting-State fMRI Data Analysis software package with t-tests and Alphasim method was performed for multiple comparison correction. Moreover, associations between ReHo values of altered brain regions and the scores of FACT-Cog were analysed by Pearson correlation. The total FACT-Cog scores and four factor scores of AML patients increased significantly after treatment. ReHo values showed no significant changes in patients before treatment when compared with HC. Compared with HC, ReHo values of the right middle frontal gyrus, inferior frontal gyrus (opercular part), middle occipital gyrus, and left praecuneus decreased significantly, while ReHo values of the left inferior temporal gyrus, right middle temporal gyrus, and hippocampus increased significantly in patients after treatment. Compared with patients before treatment, ReHo values decreased significantly in the right middle frontal gyrus, inferior frontal gyrus (opercular part), and middle and inferior occipital gyri of patients after treatment. In addition, ReHo values of the right inferior frontal gyrus (opercular part) were negatively correlated with the total scores of FACT-Cog and factor scores of perceived cognitive impairment in patients after treatment. There were also negative correlations between ReHo values of the right middle frontal gyrus and perceived cognitive impairment scores. The present study confirmed that D-CAG chemotherapy might cause impaired subjective self-reported cognitive functioning in AML patients, which might be related to the decreased function of certain regions in the right prefrontal lobe. These findings provided further understanding of the mechanisms involved in post-chemotherapy cognitive impairment and would help develop new therapeutic strategies for 'chemobrain' in AML patients.

Executive control of language in schizophrenia patients with history of auditory verbal hallucinations: A neuropsychological and resting-state fMRI study

BackgroundAs demonstrated by a plethora of studies, compromised executive functions (EF) and language are implicated in mechanisms of auditory verbal hallucinations (AVH), but the contribution of their interaction to AVH remains unclear. We hypothesized that schizophrenia patients with history of AVH (AVHh+) vs. without history of AVH (AVHh˗) have a specific deficit of executive control of language and alterations in functional connectivity (FC) between the brain regions involved in EF and language, and these neuropsychological and neurophysiological traits are associated with each other. MethodsTo explore the executive control of language and its contribution to AVH, we used an integrative approach involving analysis of neuropsychological and resting-state fMRI data of 34 AVHh+, 16 AVHh˗, and 40 healthy controls. We identified the neuropsychological and FC measures that differentiated between AVHh+, AVHh˗, and HC, and tested the associations between them. ResultsAVHh+ were characterized by decreased category and phonological verbal fluency, utterance length, productivity in the planning tasks, and poorer retelling. AVHh+ had decreased FC between the left inferior frontal gyrus and the anterior cingulate cortex. Productivity in category verbal fluency was associated with the FC between these regions. ConclusionsPoor executive control of word retrieval and deficient programming of sentence and narrative related to more general deficits of planning may be the neuropsychological traits specific for AVHh+. A neurophysiological trait specific for AVHh+ may be a decreased FC between regions involved in language production and differentiation between alien- vs. self-generated speech and between language production vs. comprehension.

FMRI fluctuations within the language network are correlated with severity of hallucinatory symptoms in schizophrenia

Although schizophrenia (SZ) represents a complex multiform psychiatric disorder, one of its most striking symptoms are auditory verbal hallucinations (AVH). While the neurophysiological origin of this pervasive symptom has been extensively studied, there is so far no consensus conclusion on the neural correlates of the vulnerability to hallucinate. With a network-based fMRI approach, following the hypothesis of altered hemispheric dominance (Crow, 1997), we expected that LN alterations might result in self-other distinction impairments in SZ patients, and lead to the distressing subjective experiences of hearing voices. We used the independent component analysis of resting-state fMRI data, to first analyze LN connectivity in three groups of participants: SZ patients with and without hallucinations (AVH/D+ and AVH/D–, respectively), and a matched healthy control (HC) group. Then, we assessed the fMRI fluctuations using additional analyses based on fractional Amplitude of Low Frequency-Fluctuations (fALFF), both at the network- and region of interest (ROI)-level. Specific LN nodes were recruited in the right hemisphere (insula and Broca homologous area) for AVH/D+ , but not for HC and AVH/D–, consistent with a left hemisphere deficit in AVH patients. The fALFF analysis at the ROI level showed a negative correlation between fALFF Slow-4 and P1 Delusions PANSS subscale and a positive correlation between the fALFF Slow-5 and P3 Hallucination PANSS subscale for AVH/D+ only. These effects were not a consequence of structural differences between groups, as morphometric analysis did not evidence any group differences. Given the role of language as an emerging property resulting from the integration of many high-level cognitive processes and the underlying cortical areas, our results suggest that LN features from fMRI connectivity and fluctuations can be a marker of neurophysiological features characterizing SZ patients depending on their vulnerability to hallucinate.

Correction to: Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

Correction to: Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

Altered local gyrification index and corresponding resting-state functional connectivity in individuals with high test anxiety

Previous studies have reported that test anxiety is closely related to unreasonable cognitive patterns and maladaptive emotional responses. However, its underlying brain structural and functional basis has not been thoroughly studied. This study aimed to evaluate the potential difference in local gyration index (LGI) and corresponding resting-state functional connectivity (RSFC) in individuals with high test anxiety (HTA) compared with low test anxiety (LTA). Twenty-six individuals with HTA and 28 individuals with LTA underwent T1-weighted structural and resting-state functional magnetic resonance imaging scans. Using FreeSurfer software, we contrasted the LGI between the HTA and LTA groups using a surface-based general linear model to map group contrasts on a vertex-by-vertex basis. By selecting the cortical regions with significant differences in the LGI analysis as the regions of interest, the seed-based RSFC analysis was further carried out using the Resting-State fMRI Data Analysis Toolkit to examine the differences in the functional connectivity of these cortical regions with the whole brain between the two groups. The results showed that the LGI in several cortical regions of the executive control network (ECN) and the right lateral occipital gyrus was lower in the HTA group than in the LTA group. Furthermore, compared with the LTA group, the HTA group exhibited abnormal RSFC within the ECN, between the ECN and the visual network, and between the ECN and the sensorimotor network. Our findings might provide preliminary evidence for brain morphology and functional alterations in individuals with HTA and contribute to a better understanding of the pathophysiology of TA.

Using deep clustering to improve fMRI dynamic functional connectivity analysis

Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly performed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means performance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occupancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised.Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subsequent clustering step. We assess the use of deep autoencoders for dimensionality reduction prior to applying k-means clustering to the encoded data. We compare this deep clustering method to dimensionality reduction using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clustering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of dimensionality reduction method has a significant effect on group-level measurements of state temporal properties.

Threat vigilance and intrinsic amygdala connectivity.

A well‐documented amygdala‐dorsomedial prefrontal circuit is theorized to promote attention to threat (“threat vigilance”). Prior research has implicated a relationship between individual differences in trait anxiety/vigilance, engagement of this circuitry, and anxiogenic features of the environment (e.g., through threat‐of‐shock and movie‐watching). In the present study, we predicted that—for those scoring high in self‐reported anxiety and a behavioral measure of threat vigilance—this circuitry is chronically engaged, even in the absence of anxiogenic stimuli. Our analyses of resting‐state fMRI data (N = 639) did not, however, provide evidence for such a relationship. Nevertheless, in our planned exploratory analyses, we saw a relationship between threat vigilance behavior (but not self‐reported anxiety) and intrinsic amygdala‐periaqueductal gray connectivity. Here, we suggest this subcortical circuitry may be chronically engaged in hypervigilant individuals, but that amygdala‐prefrontal circuitry may only be engaged in response to anxiogenic stimuli.

What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.

Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL

BackgroundA trend in the development of resting-state fMRI (rsfMRI) data analysis is the drive towards more data-driven methods. Group Independent Component Analysis (GICA) is a well-proven data-driven method for performing fMRI group analysis, though not without issues, especially the back-reconstruction from group-level independent components to individual-level components. Group information-guided ICA (GIG-ICA) and Independent Vector Analysis (IVA) are recent extensions of GICA that were shown to outperform GICA in the identification of unique rsfMRI biomarkers in psychiatric conditions. New methodIn this work, GICA, GIG-ICA, and IVA-GL analysis methods were applied to rsfMRI data acquired from 9 mice under different doses of medetomidine (0.1 – 0.3 mg/kg/h) in the before and after forepaw stimulation, and their performance was compared to determine whether GIG-ICA and IVA-GL outperform GICA in identifying robust and reliable resting-state networks in the rodent brain. ResultsOur results showed IVA-GL method had certain desirable performance characteristics over the other two methods under minimal data pre-processing and data-driven assumptions in application to analysis of mouse resting-state functional MRI. Comparison with existing methodsIVA-GL provides better stability towards detecting group differences at different model order assumptions and performed better at separating data well-defined and functionally reasonable components in mouse resting-state fMRI. At higher model order and more likely functional component assumptions, GIG-ICA and IVA-GL were found to have greater sensitivity at detecting functional connectivity changes due to physiological challenges compared to GICA. ConclusionsThis study indicates that IVA-GL yields better detection of resting-state networks in the rodent brain compared to other ICA methods and a promising data-driven analysis method for rodent rsfMRI.

The Interaction Between Caudate Nucleus and Regions Within the Theory of Mind Network as a Neural Basis for Social Intelligence.

The organization of socio-cognitive processes is a multifaceted problem for which many sophisticated concepts have been proposed. One of these concepts is social intelligence (SI), i.e., the set of abilities that allow successful interaction with other people. The theory of mind (ToM) human brain network is a good candidate for the neural substrate underlying SI since it is involved in inferring the mental states of others and ourselves and predicting or explaining others’ actions. However, the relationship of ToM to SI remains poorly explored. Our recent research revealed an association between the gray matter volume of the caudate nucleus and the degree of SI as measured by the Guilford-Sullivan test. It led us to question whether this structural peculiarity is reflected in changes to the integration of the caudate with other areas of the brain associated with socio-cognitive processes, including the ToM system. We conducted seed-based functional connectivity (FC) analysis of resting-state fMRI data for 42 subjects with the caudate as a region of interest. We found that the scores of the Guilford-Sullivan test were positively correlated with the FC between seeds in the right caudate head and two clusters located within the right superior temporal gyrus and bilateral precuneus. Both regions are known to be nodes of the ToM network. Thus, the current study demonstrates that the SI level is associated with the degree of functional integration between the ToM network and the caudate nuclei.

Multi-Modal Analysis of Resting-State fMRI Data in mTBI Patients and Association With Neuropsychological Outcomes.

Various cognitive disorders have been reported for mild traumatic brain injury (mTBI) patients during the acute stage. This acute stage provides an opportunity for clinicians to optimize treatment protocols, which are based on the evaluation of brain structural connectivity. So far, most brain functional magnetic resonance imaging studies are focused on moderate to severe traumatic brain injuries (TBIs). In this study, we prospectively collected resting state data on 50 mTBI within 3 days of injury and 50 healthy volunteers and analyzed them using Amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), graph theory methods and behavior measure, to explore the dysfunctional brain regions in acute mTBI. In our study, a total of 50 patients suffering <3 days mTBI and 50 healthy subjects were tested in rs-fMRI, as well as under neuropsychological examinations including the Wechsler Intelligence Scale and Stroop Color and Word Test. The correlation analysis was conducted between graph theoretic parameters and neuropsychological results. For the mTBI group, the ReHo of the inferior temporal gyrus and the cerebellum superior are significantly lower than in the control group, and the ALFF of the left insula, the cerebellum inferior, and the middle occipital gyrus were significantly higher than in the control group, which implies the dysfunctionality usually observed in Parkinson's disease. Executive function disorder was significantly correlated with the global efficiencies of the dorsolateral superior frontal gyrus and the anterior cingulate cortex, which is consistent with the literature: the acute mTBI patients demonstrate abnormality in terms of motor speed, association, information processing speed, attention, and short-term memory function. Correlation analysis between the neuropsychological outcomes and the network efficiency for the mTBI group indicates that executive dysfunction might be caused by local brain changes. Our data support the idea that the cerebral internal network has compensatory reactions in response to sudden pathological and neurophysiological changes. In the future, multimode rs-fMRI analysis could be a valuable tool for evaluating dysfunctional brain regions after mTBI.

Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning

PurposeTo explore the intrinsic functional connectivity (FC) alteration of the primary visual cortex (V1) between individuals with iridocyclitis and healthy controls (HCs) by the resting-state functional magnetic resonance imaging (fMRI) technique, and to investigate whether FC findings be used to differentiate patients with iridocyclitis from HCs.MethodsTwenty-six patients with iridocyclitis and twenty-eight well-matched HCs were recruited in our study and underwent resting-state fMRI examinations. The fMRI data were analyzed by Statistical Parametric Mapping (SPM12), Data Processing and Analysis for Brain Imaging (DPABI), and Resting State fMRI Data Analysis Toolkit (REST) software. Differences in FC signal values of the V1 between the individuals with iridocyclitis and HCs were compared using independent two-sample t-tests. Significant differences in FC between two groups were chosen as classification features for distinguishing individuals with iridocyclitis from HCs using a support vector machine (SVM) classifier that involved machine learning. Classifier performance was evaluated using permutation test analysis.ResultsCompared with HCs, patients with iridocyclitis displayed significantly increased FC between the left V1 and left cerebellum crus1, left cerebellum 10, bilateral inferior temporal gyrus, right hippocampus, and left superior occipital gyrus. Moreover, patients with iridocyclitis displayed significantly lower FC between the left V1 and both the bilateral calcarine and bilateral postcentral gyrus. Patients with iridocyclitis also exhibited significantly higher FC values between the right V1 and left cerebellum crus1, bilateral thalamus, and left middle temporal gyrus; while they displayed significantly lower FC between the right V1 and both the bilateral calcarine and bilateral postcentral gyrus (voxel-level P<0.01, Gaussian random field correction, cluster-level P<0.05). Our results showed that 63.46% of the participants were correctly classified using the leave-one-out cross-validation technique with an SVM classifier based on the FC of the left V1; and 67.31% of the participants were correctly classified based on the FC of the right V1 (P<0.001, non-parametric permutation test).ConclusionPatients with iridocyclitis displayed significantly disturbed FC between the V1 and various brain regions, including vision-related, somatosensory, and cognition-related regions. The FC variability could distinguish patients with iridocyclitis from HCs with substantial accuracy. These findings may aid in identifying the potential neurological mechanisms of impaired visual function in individuals with iridocyclitis.

Maladaptive brain organization at 1month into abstinence as an indicator for future relapse in patients with alcohol use disorder.

Abstinence is a lifelong endeavor, and the risk of a relapse is always present for patients with Alcohol Use Disorder (AUD). The aim of the study was to better understand specific characteristics of the intrinsic whole‐brain‐network architecture of 34 AUD patients that may support abstinence or relapse. We used Graph Theory Analysis (GTA) of resting‐state fMRI data from treatment seekers at 1 month of abstinence and their follow‐up data as abstainers or relapsers 3 months later, together with data from 30 light/non‐drinking controls scanned at the same interval. We determined the group‐specific intrinsic community configurations at both timepoints as well as the corresponding modularity Q, a GTA measure that quantifies how well individual network communities are separated from each other. Both AUD groups at both timepoints had community configurations significantly different from those of controls, but the three groups did not significantly differ in their Q values. However, relapsers showed a maladaptive community configuration at baseline, which became more similar to the controls’ community organization after the relapsers had started consuming alcohol again during the study interval. Additionally, successful recovery from AUD was not associated with re‐gaining the intrinsic brain organization found in light/non‐drinkers, but with a re‐configuration resulting in a new brain organization distinctly different from that of healthy controls. Resting‐state fMRI provides useful measures reflecting neuroplastic adaptations related to AUD treatment outcome.

Resting-State Network Plasticity Induced by Music Therapy after Traumatic Brain Injury.

Traumatic brain injury (TBI) is characterized by a complex pattern of abnormalities in resting-state functional connectivity (rsFC) and network dysfunction, which can potentially be ameliorated by rehabilitation. In our previous randomized controlled trial, we found that a 3-month neurological music therapy intervention enhanced executive function (EF) and increased grey matter volume in the right inferior frontal gyrus (IFG) in patients with moderate-to-severe TBI (N = 40). Extending this study, we performed longitudinal rsFC analyses of resting-state fMRI data using a ROI-to-ROI approach assessing within-network and between-network rsFC in the frontoparietal (FPN), dorsal attention (DAN), default mode (DMN), and salience (SAL) networks, which all have been associated with cognitive impairment after TBI. We also performed a seed-based connectivity analysis between the right IFG and whole-brain rsFC. The results showed that neurological music therapy increased the coupling between the FPN and DAN as well as between these networks and primary sensory networks. By contrast, the DMN was less connected with sensory networks after the intervention. Similarly, there was a shift towards a less connected state within the FPN and SAL networks, which are typically hyperconnected following TBI. Improvements in EF were correlated with rsFC within the FPN and between the DMN and sensorimotor networks. Finally, in the seed-based connectivity analysis, the right IFG showed increased rsFC with the right inferior parietal and left frontoparietal (Rolandic operculum) regions. Together, these results indicate that the rehabilitative effects of neurological music therapy after TBI are underpinned by a pattern of within- and between-network connectivity changes in cognitive networks as well as increased connectivity between frontal and parietal regions associated with music processing.

Brain functional changes in perimenopausal women: an amplitude of low-frequency fluctuation study.

Objective:To evaluate the effects of sex hormones on amplitude of low-frequency fluctuation (ALFF) in brain regions related to cognition in perimenopausal women.Methods:This cross-sectional study involved 25 perimenopausal women and 25 premenopausal women who underwent behavioral evaluations, sex hormone level measurements, and functional magnetic resonance imaging (fMRI). All data and ALFF analyses were preprocessed using the Data Processing Assistant for Resting-State fMRI. Statistical analyses were performed using the Resting-State fMRI Data Analysis Toolkit to explore the differences in ALFF between perimenopausal and premenopausal women. The gray matter volume (GMV) values extracted from brain regions (regions of interest) with significantly different ALFF values between the perimenopausal and premenopausal groups were compared. We analyzed the correlations of the ALFF and GMV values of these regions of interest with the results of behavioral evaluations and sex hormone levels in the two groups.Results:Compared with the premenopausal group, the perimenopausal group showed significant ALFF increase in the left gyrus rectus. Regions with decreased ALFF in the perimenopausal group included the left superior temporal gyrus, left inferior frontal gyrus, and left insula. The GMV values of the left gyrus rectus and left superior temporal gyrus were reduced in perimenopausal women. Furthermore, the estradiol level was negatively correlated with the ALFF value of the left gyrus rectus in perimenopausal women.Conclusions:The ALFF and GMV values of certain brain regions related to cognitive function were changed in perimenopausal women. Such functional brain alterations may provide more information regarding the mechanism of cognitive dysfunction in perimenopausal women.

Intrinsic functional clustering of ventral premotor F5 in the macaque brain

Neurophysiological and anatomical data suggest the existence of several functionally distinct regions in the lower arcuate sulcus and adjacent postarcuate convexity of the macaque monkey. Ventral premotor F5c lies on the postarcuate convexity and consists of a dorsal hand-related and ventral mouth-related field. The posterior bank of the lower arcuate contains two additional premotor F5 subfields at different anterior-posterior levels, F5a and F5p. Anterior to F5a, area 44 has been described as a dysgranular zone occupying the deepest part of the fundus of the inferior arcuate. Finally, area GrFO occupies the most rostral portion of the fundus and posterior bank of inferior arcuate and extends ventrally onto the frontal operculum. Recently, data-driven exploratory approaches using resting-state fMRI data have been suggested as a promising non-invasive method for examining the functional organization of the primate brain. Here, we examined to what extent partitioning schemes derived from data-driven clustering analysis of resting-state fMRI data correspond with the proposed organization of the fundus and posterior bank of the macaque arcuate sulcus, as suggested by invasive architectonical, connectional and functional investigations. Using a hierarchical clustering analysis, we could retrieve clusters corresponding to the dorsal and ventral portions of F5c on the postarcuate convexity, F5a and F5p at different antero-posterior locations on the posterior bank of the lower arcuate, area 44 in the fundus, as well as part of area GrFO in the most anterior portion of the fundus. Additionally, each of these clusters displayed distinct whole-brain functional connectivity, in line with previous anatomical tracer and seed-based functional connectivity investigations of F5/44 subdivisions. Overall, our data suggests that hierarchical clustering analysis of resting-state fMRI data can retrieve a fine-grained level of cortical organization that resembles detailed parcellation schemes derived from invasive functional and anatomical investigations.

Does an Over-Connected Visual Cortex Undermine Efforts to Stay Sober After Treatment for Alcohol Use Disorder?

A fine-tuned interplay of highly synchronized activity within and between the brain's communities is a crucial feature of the brain's functional organization. We wanted to investigate in individuals with alcohol use disorder (AUD) the degree to which the interplay of the brain's community-architecture and the extended brain reward system (eBRS) is affected by drinking status (relapse or abstinence). We used Graph Theory Analysis of resting-state fMRI data from treatment seekers at 1 month of abstinence to model the brain's intrinsic community configuration and their follow-up data as abstainers or relapsers 3 months later to quantify the degree of global across-community interaction between the eBRS and the intrinsic communities at both timepoints. After 1 month of abstinence, the ventromedial PFC in particular showed a significantly higher global across-community interaction in the 22 future relapsers when compared to 30 light/non-drinking controls. These differences were no longer present 3 months later when the relapsers had resumed drinking. We found no significant differences between abstainers and controls at either timepoint. Post hoc tests revealed that one eBRS region, the ventromedial PFC, showed a significant global across-community interaction with a community comprising the visual cortex in relapsers at baseline. In contrast, abstainers showed a significant negative association of the ventromedial PFC with the visual cortex. The increased across-community interaction of the ventromedial PFC and the visual cortex in relapsers at timepoint 1 may be an early indicator for treatment failure in a subgroup of AUD patients.

Mapping language function with task-based vs. resting-state functional MRI.

BackgroundUse of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization.PurposeTo contrast RS-fMRI vs. T-fMRI as techniques for localization of language function.MethodsWe analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses.ResultsBoth fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.ConclusionWe demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain.