RESUMO
For decades, fMRI data have been used to search for biomarkers for patients with schizophrenia. Still, firm conclusions are yet to be made, which is often attributed to the high internal heterogeneity of the disorder. A promising way to disentangle the heterogeneity is to search for subgroups of patients with more homogeneous biological profiles. We applied an unsupervised multiple co-clustering (MCC) method to identify subtypes using functional connectivity data from a multisite resting-state data set. We merged data from two publicly available databases and split the data into a discovery data set (143 patients and 143 healthy controls (HC)) and an external test data set (63 patients and 63 HC) from independent sites. On the discovery data, we investigated the stability of the clustering toward data splits and initializations. Subsequently we searched for cluster solutions, also called "views," with a significant diagnosis association and evaluated these based on their subject and feature cluster separability, and correlation to clinical manifestations as measured with the positive and negative syndrome scale (PANSS). Finally, we validated our findings by testing the diagnosis association on the external test data. A major finding of our study was that the stability of the clustering was highly dependent on variations in the data set, and even across initializations, we found only a moderate subject clustering stability. Nevertheless, we still discovered one view with a significant diagnosis association. This view reproducibly showed an overrepresentation of schizophrenia patients in three subject clusters, and one feature cluster showed a continuous trend, ranging from positive to negative connectivity values, when sorted according to the proportions of patients with schizophrenia. When investigating all patients, none of the feature clusters in the view were associated with severity of positive, negative, and generalized symptoms, indicating that the cluster solutions reflect other disease related mechanisms.
Assuntos
Encéfalo , Imageamento por Ressonância Magnética , Esquizofrenia , Esquizofrenia/fisiopatologia , Esquizofrenia/diagnóstico por imagem , Humanos , Imageamento por Ressonância Magnética/métodos , Adulto , Feminino , Masculino , Encéfalo/diagnóstico por imagem , Encéfalo/fisiopatologia , Análise por Conglomerados , Descanso/fisiologia , Bases de Dados Factuais , Reprodutibilidade dos Testes , Pessoa de Meia-IdadeRESUMO
Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.
Assuntos
Anedonia , Encéfalo/diagnóstico por imagem , Empatia/fisiologia , Esquizofrenia/diagnóstico por imagem , Percepção Social , Teoria da Mente/fisiologia , Adolescente , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Masculino , Comportamento Social , Máquina de Vetores de Suporte , Adulto JovemRESUMO
BACKGROUND: Electroencephalography (EEG) and single-pulse transcranial magnetic stimulation (spTMS) of the primary motor hand area (M1-HAND) have been combined to explore whether the instantaneous expression of pericentral mu-rhythm drives fluctuations in corticomotor excitability, but this line of research has yielded diverging results. OBJECTIVES: To re-assess the relationship between the mu-rhythm power expressed in left pericentral cortex and the amplitude of motor potentials (MEP) evoked with spTMS in left M1-HAND. METHODS: 15 non-preselected healthy young participants received spTMS to the motor hot spot of left M1-HAND. Regional expression of mu-rhythm was estimated online based on a radial source at motor hotspot and informed the timing of spTMS which was applied either during epochs belonging to the highest or lowest quartile of regionally expressed mu-power. Using MEP amplitude as dependent variable, we computed a linear mixed-effects model, which included mu-power and mu-phase at the time of stimulation and the inter-stimulus interval (ISI) as fixed effects and subject as a random effect. Mu-phase was estimated by post-hoc sorting of trials into four discrete phase bins. We performed a follow-up analysis on the same EEG-triggered MEP data set in which we isolated mu-power at the sensor level using a Laplacian montage centered on the electrode above the M1-HAND. RESULTS: Pericentral mu-power traced as radial source at motor hot spot did not significantly modulate the MEP, but mu-power determined by the surface Laplacian did, showing a positive relation between mu-power and MEP amplitude. In neither case, there was an effect of mu-phase on MEP amplitude. CONCLUSION: The relationship between cortical oscillatory activity and cortical excitability is complex and minor differences in the methodological choices may critically affect sensitivity.
Assuntos
Excitabilidade Cortical , Córtex Motor , Eletroencefalografia , Potencial Evocado Motor , Humanos , Estimulação Magnética TranscranianaRESUMO
BACKGROUND: The motor potentials evoked by transcranial magnetic stimulation (TMS) over the motor hand area (M1-HAND) show substantial inter-trial variability. Pericentral mu-rhythm oscillations, might contribute to inter-trial variability. Recent studies targeting mu-activity based on real-time electroencephalography (EEG) reported an influence of mu-power and mu-phase on the amplitude of motor evoked potentials (MEPs) in a preselected group with strong pericentral mu-activity. Other studies that determined mu-power or mu-phase based on post-hoc trial sorting according in non-preselected individuals were largely negative. OBJECTIVES: To reassess if cortico-spinal activity is modulated by the mu-rhythm, we applied single-pulse TMS to the M1-HAND conditional on the phase of the intrinsically expressed pericentral mu-rhythm in 14 non-preselected healthy young participants. METHODS: TMS was given at 0, 90, 180, and 270° of the mu-phase. Based on the absence of effects of mu-phase or mu-power when analyzing the mean MEP amplitudes, we also computed a linear mixed effects model, which included mu-phase, mu-power, inter-stimulus interval (ISIs) as fixed effects, treating the subject factor as a random effect. RESULTS: Mixed model analysis revealed a significant effect of mu-power and ISI, but no effect of mu-phase and no interactions. MEP amplitude scaled linearly with lower mu-power or longer ISIs, but these modulatory effects were very small relative to inter-trial MEP variability. CONCLUSION: Our largely negative results are in agreement with previous offline TMS-EEG studies and point to a possible influence of ISI. Future research needs to clarify under which circumstances the responsiveness of human the M1-HAND to TMS depends on the synchronicity with mu-power and mu-phase.