Comparisons of the amplitude of low-frequency fluctuation and functional connectivity in major depressive disorder and social anxiety disorder: A resting-state fMRI study.

BACKGROUND: Studies comparing the brain functions of major depressive disorder (MDD) and social anxiety disorder (SAD) at the regional and network levels remain scarce. This study aimed to elucidate their pathogenesis using neuroimaging techniques and explore biomarkers that can differentiate these disorders. METHODS: Resting-state fMRI data were collected from 48 patients with MDD, 41 patients with SAD, and 82 healthy controls. Differences in the amplitude of low-frequency fluctuations (ALFF) among the three groups were examined to identify regions showing abnormal regional spontaneous activity. A seed-based functional connectivity (FC) analysis was conducted using ALFF results as seeds and different connections were identified between regions showing abnormal local spontaneous activity and other regions. The correlation between abnormal brain function and clinical symptoms was analyzed. RESULTS: Patients with MDD and SAD exhibited similar abnormal ALFF and FC in several brain regions; notably, FC between the right superior frontal gyrus (SFG) and the right posterior supramarginal gyrus (pSMG) in patients with SAD was negatively correlated with depressive symptoms. Furthermore, patients with MDD showed higher ALFF in the right SFG than HCs and those with SAD. LIMITATION: Potential effects of medications, comorbidities, and data type could not be ignored. CONCLUSION: MDD and SAD showed common and distinct aberrant brain function patterns at the regional and network levels. At the regional level, we found that the ALFF in the right SFG was different between patients with MDD and those with SAD. At the network level, we did not find any differences between these disorders.

Aberrant baseline brain activity and disrupted functional connectivity in patients with vascular cognitive impairment due to cerebral small vessel disease.

Objective: This study aims to examine the alterations in aberrant brain activity and network connectivity between individuals with mild and major vascular cognitive impairment (VCI). Materials and methods: A total of 114 patients with cerebral small vessel disease (CSVD) were included in this study, comprising 61 individuals with mild VCI (mean age, 55.7 ± 6.9 years; male, 42.6%) and 53 cases with major VCI (mean age, 57.6 ± 5.5 years; male, 58.5%). Additionally, 53 age-, gender-, and education-matched healthy subjects were recruited as normal controls (NC) (mean age, 54.9 ± 7.9 years; male, 52.9%). All participants underwent neuropsychological assessments and magnetic resonance imaging scans. One-way analysis of variance was used to compare fractional amplitude of low-frequency fluctuation (fALFF) values among the three groups. Two-sample t-tests were conducted to assess functional connectivity matrices between different groups for each connection. Moreover, mediation analyses were performed to explore the mediating effect of aberrant brain activity on the relationship between cognitive impairment and CSVD total burden. Results: VCI patients exhibited aberrant brain activity in regions such as the right thalamus (THA_R), right cuneus (CUN_R), left postcentral gyrus (PoCG_L), right postcentral gyrus (PoCG_R), right median cingulate, paracingulate gyri (PCG_R), and left precuneus (PCUN_L). Reduced positive functional connectivity was predominantly observed among nodes including PCUN_L, CUN_R, PoCG_L, PoCG_R, right posterior cingulate (PCG_R), and left occipital gyrus (IOG_L) in VCI patients. The aberrant baseline brain activity and disrupted brain network were more pronounced with worsening cognitive function. Increased fALFF values in THA_R, CUN_R, and PoCG_L mediated cognitive impairment in CSVD patients. Conclusion: Abnormal brain activities in THA_R, CUN_R, and PoCG_L, along with their associated abnormal functional connections, play a significant role in VCI. The study revealed a progressive increase in aberrant brain activity and network connectivity with advancing stages of VCI.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy.

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

The relationship between resting-state hemodynamic (fMRI) and electrophysiological (EEG) connectivity has been investigated in healthy subjects, but this relationship is unknown in patients with left and right temporal lobe epilepsies (l/rTLE). Does the magnitude of the relationship differ between healthy subjects and patients? What role does the laterality of the epileptic focus play? What are the spatial contributions to this relationship? Here we use concurrent EEG-fMRI recordings of 65 subjects from two centers (35 controls, 34 TLE patients), to assess the correlation between EEG and fMRI connectivity. For all datasets, frequency-specific changes in cross-modal correlation were seen in lTLE and rTLE. EEG and fMRI connectivities do not measure perfectly overlapping brain networks and provide distinct information on brain networks altered in TLE, highlighting the benefit of multimodal assessment to inform about normal and pathological brain function.

Dragon boat exercise reshapes the temporal-spatial dynamics of the brain.

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and ß (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding.

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.

Cerebellar functional connectivity relates to lower urinary tract function: A 7 Tesla study.

OBJECTIVES: The objective of this study is to explore the functional connectivity (FC) of the cerebellum during the storage phase of micturition, through detecting spontaneous blood-oxygen-level dependent signal between the cerebellum and different brain regions using a high-resolution 7 Tesla magnetic resonance imaging (MRI) scanner. MATERIALS AND METHODS: We recruited healthy individuals with no reported history of neurological disease or lower urinary tract (LUT) symptoms. Participants were asked to drink 500 mL of water and then empty their bladders before entering the MRI scanner. They underwent a T1-weighted anatomical scan, followed by an initial (8 min) empty bladder resting state functional MRI (rs-fMRI) acquisition. Once subjects felt the desire to void, a second rs-fMRI scan was obtained, this time with a full bladder state. We established a priori cerebellar regions of interest from the literature to perform seed-to-voxel analysis using nonparametric statistics based on the Threshold Free Cluster Enhancement method and utilized a voxel threshold of p < 0.05. RESULTS: Twenty individuals (10 male and 10 female) with a median age of 25 years (IQR [3.5]) participated in the study. We placed 31 different 4-mm spherical seeds throughout the cerebellum and assessed their FC with the remainder of the brain. Three of these (left cerebellar tonsil, right posterolateral lobe, right posterior lobe) showed significant differences in connectivity when comparing scans conducted with a full bladder to those with an empty bladder. Additionally, we observed sex differences in FC, with connectivity being higher in women during the empty bladder condition. CONCLUSION: Our initial findings reveal, for the first time, that the connectivity of the cerebellar network is modulated by bladder filling and is associated with LUT function. Unraveling the cerebellum's role in bladder function lays the foundation for a more comprehensive understanding of urinary pathologies affecting this area.

An rs-fMRI based neuroimaging marker for adult absence epilepsy.

OBJECTIVE: Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE. METHODS: This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing. RESULTS: Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %. SIGNIFICANCE: According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

OBJECTIVE: Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. APPROACH: To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. SIGNIFICANCE: Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

Loneliness and Resting-State Functional Brain Connectivity Among Older Adults: A Proportional Correlation.

OBJECTIVE: Loneliness reportedly increases the risk of dementia, especially Alzheimer's disease (AD). The authors' previous study demonstrated associations between loneliness and structural abnormalities observed in early-stage AD. The present study examined associations between the brain's functional characteristics and loneliness among older adults with concerns about cognitive decline. METHODS: This single-center study included 43 participants (13 with amnestic mild cognitive impairment and 30 with normal cognition). Participants were assessed with the revised University of California Los Angeles (UCLA) Loneliness Scale and underwent resting-state functional MRI. Functional images were preprocessed with the CONN toolbox. The selected seeds were within brain regions reportedly associated with loneliness. One-sample general linear model analysis was performed to examine regressions of UCLA Loneliness Scale scores and functional connectivity between the seeds and regions of interest. RESULTS: The revised UCLA Loneliness Scale scores were positively correlated with functional connectivity between the right hippocampus and left lateral parietal lobe and were negatively correlated with functional connectivity between the left amygdala and left frontal operculum and between the left amygdala and right supramarginal gyrus. Analyses were adjusted for age, sex, and education and scores on the Mini-Mental State Examination and Clinical Dementia Rating scale. CONCLUSIONS: Loneliness was associated with abnormal function of the hippocampus, parts of the parietal lobe and frontal cortex, and the amygdala. These findings may suggest a possible correlation between loneliness and neurological changes associated with dementia.

Gender and age related brain structural and functional alterations in children with autism spectrum disorder.

To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.

Resting-state brain activity as a biomarker of chronic pain impairment and a mediator of its association with pain resilience.

Past cross-sectional chronic pain studies have revealed aberrant resting-state brain activity in regions involved in pain processing and affect regulation. However, there is a paucity of longitudinal research examining links of resting-state activity and pain resilience with changes in chronic pain outcomes over time. In this prospective study, we assessed the status of baseline (T1) resting-state brain activity as a biomarker of later impairment from chronic pain and a mediator of the relation between pain resilience and impairment at follow-up. One hundred forty-two adults with chronic musculoskeletal pain completed a T1 assessment comprising a resting-state functional magnetic resonance imaging scan based on regional homogeneity (ReHo) and self-report measures of demographics, pain characteristics, psychological status, pain resilience, pain severity, and pain impairment. Subsequently, pain impairment was reassessed at a 6-month follow-up (T2). Hierarchical multiple regression and mediation analyses assessed relations of T1 ReHo and pain resilience scores with changes in pain impairment. Higher T1 ReHo values in the right caudate nucleus were associated with increased pain impairment at T2, after controlling for all other statistically significant self-report measures. ReHo also partially mediated associations of T1 pain resilience dimensions with T2 pain impairment. T1 right caudate nucleus ReHo emerged as a possible biomarker of later impairment from chronic musculoskeletal pain and a neural mechanism that may help to explain why pain resilience is related to lower levels of later chronic pain impairment. Findings provide empirical foundations for prospective extensions that assess the status of ReHo activity and self-reported pain resilience as markers for later impairment from chronic pain and targets for interventions to reduce impairment. PRACTITIONER POINTS: Resting-state markers of impairment: Higher baseline (T1) regional homogeneity (ReHo) values, localized in the right caudate nucleus, were associated with exacerbations in impairment from chronic musculoskeletal pain at a 6-month follow-up, independent of T1 demographics, pain experiences, and psychological factors. Mediating role of ReHo values: ReHo values in the right caudate nucleus also mediated the relationship between baseline pain resilience levels and later pain impairment among participants. Therapeutic implications: Findings provide empirical foundations for research extensions that evaluate (1) the use of resting-state activity in assessment to identify people at risk for later impairment from pain and (2) changes in resting-state activity as biomarkers for the efficacy of treatments designed to improve resilience and reduce impairment among those in need.

MRI resting-state signature of the propensity to experience meaningful coincidences: a functional coupling analysis.

The propensity to experience meaningful patterns in random arrangements and unrelated events shows considerable interindividual differences. Reduced inhibitory control (over sensory processes) and decreased working memory capacities are associated with this trait, which implies that the activation of frontal as well as posterior brain regions may be altered during rest and working memory tasks. In addition, people experiencing more meaningful coincidences showed reduced gray matter of the left inferior frontal gyrus (IFG), which is linked to the inhibition of irrelevant information in working memory and the control and integration of multisensory information. To study deviations in the functional connectivity of the IFG with posterior associative areas, the present study investigated the fMRI resting state in a large sample of n = 101 participants. We applied seed-to-voxel analysis and found that people who perceive more meaningful coincidences showed negative functional connectivity of the left IFG (i.e. pars triangularis) with areas of the left posterior associative cortex (e.g. superior parietal cortex). A data-driven multivoxel pattern analysis further indicated that functional connectivity of a cluster located in the right cerebellum with a cluster including parts of the left middle frontal gyrus, left precentral gyrus, and the left IFG (pars opercularis) was associated with meaningful coincidences. These findings add evidence to the neurocognitive foundations of the propensity to experience meaningful coincidences, which strengthens the idea that deviations of working memory functions and inhibition of sensory and motor information explain why people experience more meaning in meaningless noise.

Resting-state neural activity and cerebral blood flow alterations in type 2 diabetes mellitus: Insights from hippocampal subfields.

OBJECTIVE: In this study, multimodal magnetic resonance imaging (MRI) imaging was used to deeply analyze the changes of hippocampal subfields perfusion and function in patients with type 2 diabetes mellitus (T2DM), aiming to provide image basis for the diagnosis of hippocampal-related nerve injury in patients with T2DM. METHODS: We recruited 35 patients with T2DM and 40 healthy control subjects (HCs). They underwent resting-state functional MRI (rs-fMRI), arterial spin labeling (ASL) scans, and a series of cognitive tests. Then, we compared the differences of two groups in the cerebral blood flow (CBF) value, amplitude of low-frequency fluctuation (ALFF) value, and regional homogeneity (ReHo) value of the bilateral hippocampus subfields. RESULTS: The CBF values of cornu ammonis area 1 (CA1), dentate gyrus (DG), and subiculum in the right hippocampus of T2DM group were significantly lower than those of HCs. The ALFF values of left hippocampal CA3, subiculum, and bilateral hippocampus amygdala transition area (HATA) were higher than those of HCs in T2DM group. The ReHo values of CA3, DG, subiculum, and HATA in the left hippocampus of T2DM group were higher than those of HCs. In the T2DM group, HbAc1 and FINS were negatively correlated with imaging characteristics in some hippocampal subregions. CONCLUSION: This study indicates that T2DM patients had decreased perfusion in the CA1, DG, and subiculum of the right hippocampus, and the right hippocampus subiculum was associated with chronic hyperglycemia. Additionally, we observed an increase in spontaneous neural activity within the left hippocampal CA3, subiculum, and bilateral HATA regions, as well as an enhanced local neural coordination in the left hippocampal CA3, DG, HATA, and subiculum among patients with type 2 diabetes, which may reflect an adaptive compensation for cognitive decline. However, this compensation may decline with the exacerbation of metabolic disorders.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Neural correlates of impulsivity in amphetamine use disorder.

Impulsivity is a trait associated with several psychiatric conditions, not least addictive disorders. While the neural mechanisms behind certain aspects of impulsivity have been studied extensively, there are few imaging studies examining this neurocircuitry in populations with substance use disorders. Therefore, we aimed to examine the functional connectivity of relevant neural networks, and their possible association with trait impulsivity, in a sample with severe amphetamine use disorder and a control group of healthy subjects. We used data collected in a randomized clinical trial studying the acute effects of oral naltrexone in amphetamine use disorder. Our final sample included 32 amphetamine users and 27 healthy controls. Trait impulsivity was rated with the Barratt Impulsiveness Scale-11, and functional connectivity was measured during resting-state fMRI, looking specifically at networks involving prefrontal regions previously implicated in studies of impulsivity. Amphetamine users had higher subjective ratings of impulsivity as compared to healthy controls, and these scores correlated positively with a wide-spread prefrontal hyperconnectivity that was found among the amphetamine users. These findings highlight the importance of aberrant prefrontal function in severe addiction.

Speech-language within and between network disruptions in primary progressive aphasia variants.

Primary progressive aphasia (PPA) variants present with distinct disruptions in speech-language functions with little known about the interplay between affected and spared regions within the speech-language network and their interaction with other functional networks. The Neurodegenerative Research Group, Mayo Clinic, recruited 123 patients with PPA (55 logopenic (lvPPA), 44 non-fluent (nfvPPA) and 24 semantic (svPPA)) who were matched to 60 healthy controls. We investigated functional connectivity disruptions between regions within the left-speech-language network (Broca, Wernicke, anterior middle temporal gyrus (aMTG), supplementary motor area (SMA), planum temporale (PT) and parietal operculum (PO)), and disruptions to other networks (visual association, dorsal-attention, frontoparietal and default mode networks (DMN)). Within the speech-language network, multivariate linear regression models showed reduced aMTG-Broca connectivity in all variants, with lvPPA and nfvPPA findings remaining significant after Bonferroni correction. Additional loss in Wernicke-Broca connectivity in nfvPPA, Wernicke-PT connectivity in lvPPA and greater aMTG-PT connectivity in svPPA were also noted. Between-network connectivity findings in all variants showed reduced aMTG-DMN and increased aMTG-dorsal-attention connectivity, with additional disruptions between aMTG-visual association in both lvPPA and svPPA, aMTG-frontoparietal in lvPPA, and Wernicke-DMN breakdown in svPPA. These findings suggest that aMTG connectivity breakdown is a shared feature in all PPA variants, with lvPPA showing more extensive connectivity disruptions with other networks.

Passive hyperthermia alters the resting-state functional connectivity of mouse brain.

PURPOSE: To investigate how passive hyperthermia affect the resting-state functional brain activity based on an acute mouse model after heat stress exposure. MATERIALS AND METHODS: Twenty-eight rs-fMRI data of C57BL/6J male mice which weighing about 24 ∼ 29 g and aged 12 ∼ 16 weeks were collected. The mice in the hyperthermia group (HT, 40 °C ± 0.5 °C, 40 min) were subjected to passive hyperthermia before the anesthesia preparation for scanning. While the normal control group (NC) was subjected to normothermia condition (NC, 20 °C ± 2 °C, 40 min). After data preprocessing, we performed independent component analysis (ICA) and region of interested (ROI)-ROI functional connectivity (FC) analyses on the data of both HT (n = 13) and NC (n = 15). RESULTS: The group ICA analysis showed that the HT and the NC both included 11 intrinsic connectivity networks (ICNs), and can be divided into four types of networks: the cortical network (CN), the subcortical network (SN), the default mode network (DMN), and cerebellar networks. CN and SN belongs to sensorimotor network. Compared with NC, the functional network organization of ICNs in the HT was altered and the overall functional intensity was decreased. Furthermore, 13 ROIs were selected in CN, SN, and DMN for further ROI-ROI FC analysis. The ROI-ROI FC analysis showed that passive hyperthermia exposure significantly reduced the FC strength in the overall brain represented by CN, SN, DMN of mice. CONCLUSION: Prolonged exposure to high temperature has a greater impact on the overall perception and cognitive level of mice, which might help understand the relationship between neuronal activities and physiological thermal sensation and regulation as well as behavioral changes.

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study.

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.