Impaired prefrontal cortex-thalamus pathway in intractable temporal lobe epilepsy with aberrant executive control function: MRI evidence

ObjectivesThis study aimed to assess structural and functional connectivity alterations of the prefrontal cortex (PFC)-thalamus axis in individuals with unilateral intractable temporal lobe epilepsy (TLE) showing executive control function (ECF) impairment and to explore the potential mechanism.MethodsThirty-eight individuals with intractable left TLE and twenty-nine healthy controls (HCs) were recruited for diffusion tensor imaging (DTI) and resting-state fMRI (rs-fMRI) scanning. According to the ECF state, patients were assigned to normal and impaired ECF groups. Functional connectivity (FC) and probabilistic diffusion tractography of the PFC- thalamus pathway were assessed. The general linear model (GLM) was employed for comparing fiber number (FN) and FC between groups. Pearson correlation analysis of FC, FN and ECF test scores was performed.ResultsFC and FN of left DLPFC-thalamus pathway were significantly increased in the impaired ECF group compared with the normal ECF and HC groups. However, FC and FN were not correlated with ECF score.ConclusionsThese findings indicated increased connectivity between DLPFC and the ipsilateral thalamus might reflect nonfunctional nerve remodeling along the seizure pathway.SignificanceThe present findings suggest that the DLPFC-thalamus pathway may be an important structure for exploring the mechanisms of TLE with ECF dysfunction.     

Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan

The transition from early adulthood to the elder is marked by functional and structural brain transformations. Many previous studies examined the correlation between the functional connectivity (FC) and aging using resting‐state fMRI. Results showed that the changes in FC are linked to aging as well as the cognitive ability changes. However, some researchers proposed that the FC is not static but dynamic changes during the resting‐state fMRI scan. In this study, we examined the correlation between the resting‐state dynamic functional network connectivity and age using resting scan data of 434 subjects. The results suggested: (a) age is negatively associated with variability of dynamic functional network connectivity state; (b) the dwell time of each age range spends in each state is different; (c) the dynamic graph metrics curve of each age ranges is different and 19–30 age range has the largest average global efficiency and average local efficiency; (d) some dynamic functional network connectivity measures were correlated to the certain cognitive ability. Overall, the results suggested the changes in dynamic functional network connectivity measures may be a characteristic of the aging process and in further investigations may provide a deep understanding of the aging process.     

Functional connectivity of major depression disorder using ongoing EEG during music perception

ObjectiveThe functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD patients exposed to conditions of music perception using ongoing electroencephalogram (EEG).MethodsFirst, we applied the phase lag index (PLI) method to calculate the connectivity matrices and graph theory-based methods to measure the topology of brain networks across different frequency bands. Then, classification methods were adopted to identify the most discriminate frequency band for the diagnosis of MDD.ResultsDuring music perception, MDD patients exhibited a decreased connectivity pattern in the delta band but an increased connectivity pattern in the beta band. Healthy people showed a left hemisphere-dominant phenomenon, but MDD patients did not show such a lateralized effect. Support vector machine (SVM) achieved the best classification performance in the beta frequency band with an accuracy of 89.7%, sensitivity of 89.4% and specificity of 89.9%.ConclusionsMDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD.SignificanceOur study provided a promising reference for the diagnosis of MDD, and revealed a new perspective for understanding the topology of MDD brain networks during music perception.     

Characterization of functional brain connectivity towards optimization of music selection for therapy: a fMRI study

Abstract

Background: Music therapy, a nontraditional approach to patient care, has long been used to achieve a wide variety of positive results. To deepen our understanding of the connection and therapeutic potential of music, the effect of music therapy and music medicine (music administered to individuals without an interactive therapeutic relationship) on the brain remains a topic of active research.

Objective: This study is aimed at investigating the effect of different music genres and individualized music selection on brain functional connectivity (FC) measured by functional magnetic resonance imaging (fMRI).

Methods: Twelve healthy subjects listened to five excerpts: Bach with and without visual guide (unfamiliar), self-selected familiar music, Gagaku (unfamiliar music) and Chaplin (spoken word) while undergoing a block design fMRI study. fMRI datasets were imported into CONN (Matlab toolbox) and graph networks were created for 132 anatomical regions in MNI space. Group connectivity for each soundtrack was quantified and statistically analyzed using the R package.

Results: Complex interactions between brain regions, cerebellar regions (713), superior frontal gyrus (178) and parahippocampus (223), were highest for self-selected music. Brain regions involving sound processing, memory retrieval, semantic processing and motor areas were continuously activated for all five excerpts; however, most connections were formed in language processing regions for the Bach excerpt.

Conclusion: Functional brain connectivity varied by soundtrack with the largest degree of connectivity found consistently for self-selected and unfamiliar (Bach, Gagaku) music. Incorporating individualized music listening into existing therapy paradigms may positively contribute to standard protocol for stroke rehabilitation and prevention.     

Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine-dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine-dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs). Eight ICs were selected manually as relevant brain networks, which were used to classify healthy versus cocaine-dependent participants. FC and DFC measures of the chosen IC pairs were used as features for the classification algorithm. Support Vector Machines were used for both feature selection/reduction and participant classification. Based on DFC with only seven IC pairs, participants were successfully classified with 95% accuracy (and with 90% accuracy with three IC pairs), whereas static FC yielded only 81% accuracy. Visual, sensorimotor, default mode, and executive control networks, amygdala, and insula played the most significant role in the DFC-based classification. These findings support the use of DFC-based classification of fMRI data as a potential biomarker for the identification of cocaine dependence.     

A multi-domain prognostic model of disorder of consciousness using resting-state fMRI and laboratory parameters

Objectives

Although laboratory parameters have long been recognized as indicators of outcome of traumatic brain injury (TBI), it remains a challenge to predict the recovery of disorder of consciousness (DOC) in severe brain injury including TBI. Recent advances have shown an association between alterations in brain connectivity and recovery from DOC. In the present study, we developed a prognostic model of DOC recovery via a combination of laboratory parameters and resting-state functional magnetic resonance imaging (fMRI).

Methods

Fifty-one patients with DOC (age = 52.3 ± 15.2 y, male/female = 31/20) were recruited from Hangzhou Hospital of Zhejiang CAPR and were sub-grouped into conscious (n = 34) and unconscious (n = 17) groups based upon their Glasgow Outcome Scale-Extended (GOS-E) scores at 12-month follow-ups after injury. Resting-state functional connectivity, network nodal measures (centrality), and laboratory parameters were obtained from each patient and served as features for support vector machine (SVM) classifications.

Results

We found that functional connectivity was the most accurate single-domain model (ACC: 70.1% ± 4.5%, P = 0.038, 1000 permutations), followed by degree centrality, betweenness centrality, and laboratory parameters. The stacked multi-domain prognostic model (ACC: 73.4% ± 3.1%, P = 0.005, 1000 permutations) combining all single-domain models yielded a significantly higher accuracy compared to that of the best-performing single-domain model (P = 0.002).

Conclusion

Our results suggest that laboratory parameters only contribute to the outcome prediction of DOC patients, whereas combining information from neuroimaging and clinical parameters may represent a strategy to achieve a more accurate prognostic model, which may further provide better guidance for clinical management of DOC patients.

     

The impact of hippocampal impairment on task-positive and task-negative language networks in temporal lobe epilepsy

ObjectiveTo study hippocampal integration within task-positive and task-negative language networks and the impact of a diseased left and right hippocampus on the language connectome in temporal lobe epilepsy (TLE).MethodsWe used functional magnetic resonance imaging (fMRI) to study a homogenous group of 32 patients with TLE (17 left) and 14 healthy controls during a verb-generation task. We performed functional connectivity analysis and quantified alterations within the language connectome and evaluated disruptions of the functional dissociation along the anterior-posterior axis of the hippocampi.ResultsConnectivity analysis revealed significant differences between left and right TLE compared to healthy controls. Left TLE showed widespread impairment of task-positive language networks, while right TLE showed less pronounced alterations. Particularly right TLE showed altered connectivity for cortical regions that were part of the default mode network (DMN). Left TLE showed a disturbed functional dissociation pattern along the left hippocampus to left and right inferior frontal language regions, while left and right TLE revealed an altered dissociation pattern along the right hippocampus to regions associated with the DMN.ConclusionsOur results showed an impaired hippocampal integration into active language and the default mode networks, which both may contribute to language impairment in TLE.SignificanceOur results emphasize the direct role of the left hippocampus in language processing, and the potential role of the right hippocampus as a modulator between DMN and task-positive networks.     

Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning

Objectives: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity.

In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM).

Methods: We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology.

Results: Subjects with atypical brain network organisation had higher levels of psychopathology (p?Conclusions: The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.     

Diagnostic power of resting‐state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer''s disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.     

Validating dynamicity in resting state fMRI with activation‐informed temporal segmentation

Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test–retest reliability. We hypothesize that time‐varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data‐driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole‐brain functional activation, rather than a fixed‐length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block‐design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject‐specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole‐brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time‐varying FC in rest.     

Dynamic functional connectivity in modular organization of the hippocampal network marks memory phenotypes in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a network disorder with a high incidence of memory impairment. Memory processing ability highly depends on the dynamic coordination between distinct modules within the hippocampal network. Here, we investigate the relationship between memory phenotypes and modular alterations of dynamic functional connectivity (FC) in the hippocampal network in TLE patients. Then, 31 healthy controls and 66 TLE patients with hippocampal sclerosis were recruited. The patients were classified into memory‐intact (MI, 35 cases) group and memory‐deficit (MD, 31 cases) group, each based on individual''s Wechsler Memory Scale‐Revised score. The sliding‐windows approach and graph theory analysis were used to analyze the hippocampal network based on resting state functional magnetic resonance imaging. Temporal properties and modular metrics were calculated. Two discrete and switchable states were revealed: a high modularized state (State I) and a low modularized state (State II), which corresponded to either anterior or posterior hippocampal network dominated pattern. TLE was prone to drive less State I but more State II, and the tendency was more obvious in TLE‐MD. Additionally, TLE‐MD showed more widespread alterations of modular properties compared with TLE‐MI across two states. Furthermore, the dynamic modularity features had unique superiority in discriminating TLE‐MD from TLE‐MI. These findings demonstrated that state transitions and modular function of dissociable hippocampal networks were altered in TLE and more importantly, they could reflect different memory phenotypes. The trend revealed potential values of dynamic FC in elucidating the mechanism underlying memory impairments in TLE.     

Decreased resting-state interhemispheric functional connectivity in unaffected siblings of schizophrenia patients

BackgroundNeuroimaging studies in unaffected siblings of schizophrenia patients can provide clues to the pathophysiology for the development of schizophrenia. However, little is known about the alterations of the interhemispheric resting-state functional connectivity (FC) in siblings, although the dysconnectivity hypothesis is prevailing in schizophrenia for years. In the present study, we used a newly validated voxel-mirrored homotopic connectivity (VMHC) method to identify whether aberrant interhemispheric FC was present in unaffected siblings at increased risk of developing schizophrenia at rest.MethodsForty-six unaffected siblings of schizophrenia patients and 50 age-, sex-, and education-matched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI). Automated VMHC was used to analyze the data.ResultsThe sibling group had lower VMHC than the control group in the angular gyrus (AG) and the lingual gyrus/cerebellum lobule VI. No region exhibited higher VMHC in the sibling group than in the control group. There was no significant sex difference of the VMHC values between male siblings and female siblings or between male controls and female controls, although evidence has been accumulated that size and shape of the corpus callosum, and functional homotopy differ between men and women.ConclusionsOur results first suggest that interhemispheric resting-state FC of VMHC is disrupted in unaffected siblings of schizophrenia patients, and add a new clue of abnormal interhemispheric resting-state FC to the pathophysiology for the development of schizophrenia.     

Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI

ObjectiveThis study aimed to develop an automatic classifier to distinguish different motor subtypes of Parkinson's disease (PD) based on multilevel indices of resting-state functional magnetic resonance imaging (rs-fMRI).MethodsNinety-six PD patients, which included thirty-nine postural instability and gait difficulty (PIGD) subtype and fifty-seven tremor-dominant (TD) subtype, were enrolled and allocated to training and validation datasets with a ratio of 7:3. A total of five types of index, consisting of mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and functional connectivity (FC), were extracted. The features were then selected using a two-sample t-test, the least absolute shrinkage and selection operator (LASSO), and Spearman's rank correlation coefficient. Finally, support vector machine (SVM) models based on the separate index and multilevel indices were built, and the performance of models was assessed via the area under the receiver operating characteristic curve (AUC). Feature importance was evaluated using Shapley additive explanation (SHAP) values.ResultsThe optimal SVM model was obtained based on multilevel rs-fMRI indices, with an AUC of 0.934 in the training dataset and an AUC of 0.917 in the validation dataset. The AUCs of the models based on the separate index were ranged from 0.783 to 0.858 for the training dataset and from 0.713 to 0.792 for the validation dataset. SHAP analysis revealed that functional activity and connectivity in frontal lobe and cerebellum were important features for differentiating PD subtypes.ConclusionsOur findings demonstrated multilevel rs-fMRI indices could provide more comprehensive information on brain functionalteration. Furthermore, the machine learning method based on multilevel rs-fMRI indices might be served as an alternative approach for automatically classifying clinical subtypes in PD at the individual level.     

Temporal lobe epilepsy: Decreased thalamic resting-state functional connectivity and their relationships with alertness performance

ObjectivesStudies have provided evidence regarding the pathology of the thalamus in patients with temporal lobe epilepsy (TLE). The thalamus, particularly the right thalamus, is one of the subcortical structures that are most uniformly accepted as being significantly involved in alertness. Moreover, alertness impairment in epilepsy has been reported. This study aimed to investigate alterations in thalamic resting-state functional connectivity (FC) and their relationships with alertness performance in patients with TLE; an issue that has not yet been addressed.MethodsA total of 15 patients with right TLE (rTLE) and 16 healthy controls were recruited for the present study. All of the participants underwent a resting-state functional magnetic resonance imaging (fMRI) scan and the attention network test (ANT). Whole-brain voxel-wise FC analyses were applied to extract the thalamic resting-state functional networks in the patients with rTLE and healthy controls, and the differences between the two groups were evaluated. Correlation analyses were employed to examine the relationships between alterations in thalamic FC and alertness performance in patients with rTLE.ResultsCompared to the healthy controls, the FC within and between the bilateral thalamus was decreased in the patients with rTLE. Moreover, in the patient group, the bilateral anterior cingulate cortex (ACC) and subcortical regions, including the bilateral brainstem, cerebellum, putamen, right caudate nucleus, and amygdala, exhibited decreased FC with the ipsilateral thalamus (p < 0.05, AlphaSim corrected, cluster size > 44) but not with the contralateral thalamus (p < 0.05, AlphaSim corrected, cluster size > 43). The intrinsic and phasic alertness performances of the patients were impaired (p = 0.001 and p < 0.001, respectively) but not correlated with decreased thalamic FC. Meanwhile, the alertness performance was not altered in right TLE but was negatively correlated with decreased thalamic FC with ACC (p < 0.05).ConclusionsOur findings highlight the functional importance of the thalamus in TLE pathology and suggest that damage to the thalamic resting-state functional networks, particularly ipsilateral to the epileptogenic focus, is present in patients with TLE.     

Altered dynamic functional connectivity in weakly-connected state in major depressive disorder

ObjectiveMajor depressive disorder (MDD) is accompanied by abnormal changes in dynamic functional connectivity (FC) among brain regions. The aim of this study is to investigate whether the abnormalities of dynamic FC in MDD are state-dependent (related to a specific connectivity state).MethodsWe performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 49 MDD patients and 54 matched healthy controls (HCs). FC differences between groups in each connectivity state were analyzed and associations between disease severity and dynamics of aberrant FC were explored.ResultsTwo distinct connectivity states (i.e., weakly-connected and strongly-connected state) were identified. Compared to HCs, MDD patients were associated with increased mean dwell time and decreased FC between and within subnetworks in the weakly-connected state. Dynamics of reduced FC between cognitive control network and default mode network as well as within cognitive control network predicted individual differences in depression symptom severity.ConclusionsOur findings suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD.SignificanceThese findings provide new perspectives for understanding the state-dependent neurophysiological mechanisms in MDD.     

Dynamic functional connectivity impairments in idiopathic rapid eye movement sleep behavior disorder

IntroductionPrevious functional magnetic resonance imaging (fMRI) studies typically analyzed static functional connectivity (sFC) to reveal the pathophysiology of iRBD and overlooked the dynamic nature of brain activity. Thus, we aimed to explore whether iRBD showed abnormalities of brain network dynamics using the dynamic functional connectivity (dFC) approach.MethodsResting-state fMRI data from 33 iRBD patients and 38 matched healthy controls were analyzed using an independent component analysis, sliding window correlation and k-means clustering. Relationships between clinical symptoms and abnormal dFC were evaluated using Spearman's correlation analysis.ResultsFour distinct connectivity states were identified to characterize and compare dFC patterns. We demonstrated that iRBD had fewer occurrences and a shorter dwell time in the infrequent and strongly connected State 1, but with more occurrences and a longer dwell time in the frequent and sparsely connected State 2. In addition, iRBD patients showed significantly decreased FC in certain dFC states compared to healthy controls. More importantly, the impairments in the temporal properties of State 2 were found to be associated RBDSQ scores in the patient group.ConclusionsThis study detected dFC impairments in iRBD patients and provided new insights into the pathophysiology of iRBD, which might contribute to the development of disease-modifying drugs in future clinical trials.     

Investigating the effects of subconcussion on functional connectivity using mass-univariate and multivariate approaches

There are concerns about the effects of subconcussive head impacts in sport, but the effects of subconcussion on brain connectivity are not well understood. We hypothesized that college football players experience changes in brain functional connectivity not found in athletes competing in lower impact sports or healthy controls. These changes may be spatially heterogeneous across participants, requiring analysis methods that go beyond mass-univariate approaches commonly used in functional MRI (fMRI). To test this hypothesis, we analyzed resting-state fMRI data from college football (n?=?15), soccer (n?=?12), and lacrosse players (n?=?16), and controls (n?=?29) collected at preseason and postseason time points. Regional homogeneity (ReHo) and degree centrality (DC) were calculated as measures of local and long-range functional connectivity, respectively. Standard voxel-wise analysis and paired support vector machine (SVM) classification studied subconcussion’s effects on local and global functional connectivity. Voxel-wise analyses yielded minimal findings, but SVM classification had high accuracy for college football’s ReHo (87%, p?=?0.009) and no other group. The findings suggest subconcussion results in spatially heterogeneous changes in local functional connectivity that may only be detectible with multivariate analyses. To determine if voxel-wise and SVM analyses had similar spatial patterns, region-average t-statistic and SVM weight values were compared using a measure of ranking distance. T-statistic and SVM weight rankings exhibited significantly low ranking distance values for all groups and metrics, demonstrating that the analyses converged on a similar underlying effect. Overall, this research suggests that subconcussion in football may produce local functional connectivity changes similar to concussion.     

Aberrant brain functional hubs and causal connectivity in presbycusis

To investigate resting-state connectivity and further understand directional aspects of implicit alterations in presbycusis patients, we used degree centrality (DC) and Granger causality analysis (GCA) to detect functional hubs of the whole-brain network and then analyze directional connectivity. Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 40 presbycusis patients and 40 healthy controls matched for age, gender, and education. We used DC analysis and GCA to characterize abnormal brain networks in presbycusis patients. The associations of network centrality and directed functional connectivity (FC) with clinical measures of presbycusis were also examined according to the above results. We found that the network centrality of left frontal middle gyrus (MFG) was significantly lower than that of healthy control group. Unidirectionally, the left MFG revealed increased directional connectivity to the left superior frontal gyrus (SFG), while the left MFG exhibited decreased directional connectivity to the left middle temporal gyrus (MTG) and right lingual gyrus (LinG). And the decreased directional connectivity was found from the left precentral gyrus (PrCG) to the left MFG. In addition, the Trail-Making Test B (TMT-B) score was negatively correlated with the decreased DC of the left MFG (r?=??0.359, p?=?0.032). Resting-state fMRI provides a novel method for identifying aberrant brain network architecture. These results primarily indicate altered functional hubs and abnormal frontal lobe connectivity patterns that may further reflect executive dysfunction in patients with presbycusis.

     

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

ObjectiveChildhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.MethodsTwelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks.ResultsDelta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs.ConclusionUsing spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring.SignificanceIdentifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.     

Dynamic frontostriatal functional peak connectivity (in alcohol use disorder)

Alcohol use disorder (AUD) is associated with changes in frontostriatal connectivity, but functional magnetic resonance imaging (fMRI) functional connectivity (FC) approaches are usually not adapted to these circuits. We developed a circuit‐specific fMRI analysis approach to detect dynamic changes in frontostriatal FC inspired by medial‐ventral‐rostral to lateral‐dorsal‐caudal frontostriatal gradients originally identified in nonhuman primate tract‐tracing data. In our PeaCoG (“ pea k co nnectivity on a g radient”) approach we use information about the location of strongest FC on empirical frontostriatal connectivity gradients. We have recently described a basic PeaCoG version with conventional FC, and now developed a dynamic PeaCoG approach with sliding‐window FC. In resting state data of n = 66 AUD participants and n = 40 healthy controls we continue here the analyses that we began with the basic version. Our former result of an AUD‐associated ventral shift in right orbitofrontal cortex PeaCoG is consistently detected in the dynamic approach. Temporospatial variability of dynamic PeaCoG in the left dorsolateral prefrontal cortex is reduced in AUD and associated with self‐efficacy to abstain and days of abstinence. Our method has the potential to provide insight into the dynamics of frontostriatal circuits, which has so far been relatively unexplored, and into their role in mental disorders and normal cognition.