A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Cortex, Год журнала: 2024, Номер 173, С. 313 - 332

Опубликована: Фев. 21, 2024

Язык: Английский

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Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

Journal of Affective Disorders, Год журнала: 2024, Номер 359, С. 140 - 144

Опубликована: Май 14, 2024

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological biological factors is critical elucidating its etiology persistence. We aimed to evaluate utility of using symptom-brain networks parse heterogeneity depressive complaints a large adolescent sample. used data from third wave IMAGEN study, multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 0.72). Two network models were estimated: one including an overall severity sum score based on Adolescent Depression Rating Scale (ADRS), incorporating individual ADRS symptom/item scores. Both included measures cortical thickness several regions (insula, cingulate, mOFC, fusiform gyrus) hippocampal volume derived neuroimaging. The scores revealed associations specific complaints, obscured when aggregate depression score. Notably, insula's showed negative with cognitive dysfunction (partial cor. −0.15); cingulate's feelings worthlessness −0.10), mOFC was negatively associated anhedonia −0.05). This cross-sectional relied self-reported assessment non-clinical sample predominantly healthy participants (19 or sub-threshold depression). showcases parsing linking neural substrates. outline next steps integrate markers unravel MDD's phenotypic heterogeneity.

Язык: Английский

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The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

Neuroscience & Biobehavioral Reviews, Год журнала: 2024, Номер 165, С. 105846 - 105846

Опубликована: Авг. 6, 2024

The large number of different analytical choices used by researchers is partly responsible for the challenge replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge full space options essential. We conducted a systematic literature review to identify decisions functional data preprocessing and analysis emerging field cognitive network neuroscience. found 61 steps, with 17 them having debatable parameter choices. Scrubbing, global signal regression, spatial smoothing are among controversial steps. There no standardized order which steps applied, settings within several vary widely across By aggregating pipelines studies, we propose three taxonomic levels categorize choices: 1) inclusion or exclusion specific 2) tuning 3) distinct sequencing have developed decision support application high educational value called METEOR facilitate access design well-informed (multiverse) analysis.

Язык: Английский

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Getting stress-related disorders under control: the untapped potential of neurofeedback

Trends in Neurosciences, Год журнала: 2024, Номер 47(10), С. 766 - 776

Опубликована: Сен. 10, 2024

Язык: Английский

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MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings

Cell Reports Methods, Год журнала: 2024, Номер 4(11), С. 100901 - 100901

Опубликована: Ноя. 1, 2024

MotivationMicroelectrode array (MEA) recordings of neuronal activity are an essential functional assay for evaluating in vitro models neurodevelopment and neurological diseases. However, most studies limited to comparing firing burst rates. We have previously shown that 3D human cerebral organoids develop microscale networks. The network-level features, which predict cellular-scale information processing efficiency, can provide a bioinformatic phenotype network function MEA from tissues. Broader application connectivity, topology, dynamics analysis 2D human-derived or murine cultures could advance mechanistic therapeutic studies, particularly disease models. Our user-friendly, open-source pipeline, MEA-NAP, addresses current gap computational tools studying function.Highlights•MEA-NAP identifies features microelectrode recordings•We use MEA-NAP track development mouse cultures•Human iPSC-derived cultured neural networks increase size density•MEA-NAP reveals developing hub roles with node cartographySummaryMicroelectrode commonly used compare rates cultures. also reveal dynamics—patterns seen brain across spatial scales. Network topology is frequently characterized neuroimaging graph theoretical metrics. few exist analyzing recordings. Here, we present MATLAB pipeline (MEA-NAP) raw voltage time series acquired single- multi-well MEAs. Applications differences development, including cartography, dimensionality. incorporates multi-unit template-based spike detection, probabilistic thresholding determining significant connections, normalization techniques identify effects pharmacologic perturbation and/or disease-causing mutations thus translational platform revealing insights screening new approaches.Video abstract/cms/asset/e47b5239-ccd6-47f5-a237-7695cd7c579a/mmc2.mp4Loading ...Download video (mp4, 292 MB)Graphical abstract

Язык: Английский

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Influence of Trainees' Transfer Beliefs, Intentions, and Commitment on Transfer Readiness: Variable and Person‐Oriented Analyses

International Journal of Training and Development, Год журнала: 2025, Номер unknown

Опубликована: Фев. 4, 2025

ABSTRACT Transfer beliefs are understudied in the training transfer field, whereas structural equation modelling (SEM) has been a widely used technique to study models. New methodologies needed and network analysis (NA) emerged as new approach that provides visual representation of given network. We explored relation beliefs, intentions, commitment, implementation using variable person‐oriented approaches according groups trainees based on their readiness. The longitudinal design measured T1 before T2 after (268 participants). trainees' about transfer, commitment intention transfer; self‐reported actions. results NA confirmed structure exploratory factor analysis. model offered complimentary obtained via SEM. Differentiating SEM multigroup by cluster showed differences models architectures between clusters. discussed relations also implications combined use novel transfer.

Язык: Английский

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Severity of anhedonia is associated with hyper-synchronization of the salience-default mode network in non-clinical individuals: a resting state EEG connectivity study

Journal of Neural Transmission, Год журнала: 2025, Номер unknown

Опубликована: Фев. 15, 2025

Anhedonia is a core transnosographic symptom in several neuropsychiatric disorders. Recently, the Triple Network (TN) model has been proposed as useful neurophysiological paradigm for conceptualizing anhedonia, providing new insights to clinicians and researchers. Despite this, relationship between functional dynamics of TN severity anhedonia relatively understudied non-clinical samples, especially resting state (RS) condition. Therefore, current study, we investigated this using electroencephalography (EEG) connectivity. Eighty-two participants (36 males; mean age: 24.28 ± 7.35 years) underwent RS EEG recording with eyes-closed completed Beck Depression Inventory-derived 4-item scale (BDI-Anh4) Brief Symptoms Inventory (BSI). data on connectivity were analyzed exact low-resolution electromagnetic tomography (eLORETA). A significant positive correlation was observed BDI-Anh4 total score salience-default mode network beta frequency band (r = 0.409; p 0.010). The results hierarchical linear regression analysis also showed that pattern positively independently associated (β 0.358; < 0.001) explained an additional 11% variability. association increased synchronization detected study may reflect difficulty disengaging from internal/self-related mental contents, which consequently impairs processing other stimuli, including rewarding stimuli.

Язык: Английский

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Relationships and representations of brain structures, connectivity, dynamics and functions

Progress in Neuro-Psychopharmacology and Biological Psychiatry, Год журнала: 2025, Номер unknown, С. 111332 - 111332

Опубликована: Март 1, 2025

Язык: Английский

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Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis

NeuroImage Clinical, Год журнала: 2025, Номер unknown, С. 103785 - 103785

Опубликована: Апрель 1, 2025

Understanding complex brain-behaviour relationships in psychiatric and neurological conditions is crucial for advancing clinical insights. This review explores the current landscape of network estimation methods context functional MRI (fMRI) based neuroscience, focusing on static undirected analysis. We focused papers published a single year (2022) characterised what we consider fundamental building blocks analysis: sample size, association type, edge inclusion strategy, weights, modelling level, confounding factors. found that most common across all included studies (n = 191) were use pairwise correlations to estimate associations between brain regions (79.6 %), weighted networks (95.3 at individual level (86.9 %). Importantly, substantial number lacked comprehensive reporting their methodological choices, hindering synthesis research findings within field. underscores critical need careful consideration transparent fMRI methodologies advance our understanding relationships. By facilitating integration neuroscience psychometrics, aim significantly enhance these intricate connections.

Язык: Английский

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First vs recurrent episode symptomatology in Major Depressive Disorder and its relation to brain function and structure: a network approach

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Апрель 18, 2025

Abstract Background. Major Depressive Disorder (MDD) is a prevalent psychiatric disorder. At least half of the patients who recover from first depressive episode, will experience relapse. Therefore, understanding underlying mechanisms supporting relapse clinical urgency that could be informed by studying complex brain-behavior associations. Here, we investigated how relationships between symptomatology and regional brain characteristics differed people with episode vs recurrent depression. Methods. We used REST-meta-MDD data DIRECT consortium. focused on comparing global local network properties (n=239) (n=179) on: (i) symptom network, (ii) structural (VBM) functional networks (ALFF, ReHO), (iii) integrated symptoms using psychopathology multimodal approach. Results. Symptom analysis showed high values strength centrality for “Insomnia: Early Hours Morning” “General somatic symptoms” at recurrence compared to episode. Also, differences in symptom-brain (measured ReHo metric) (S=2.09 p = 0.042). Finally, found edge specific links, including insomnia symptoms-, differ recurrence. Conclusions. For networks, but not differentiated MDD, specially stronger relations reflecting integrity (ReHO) was related This suggests have relevance brain-symptom underpinning

Язык: Английский

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