The structural connectome constrains fast brain dynamics

et al. 2022

Preprint

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Amyotrophic lateral sclerosis (ALS) is a multisystem disorder. This view is widely supported by clinical, molecular and neuroimaging evidence. As a consequence, predicting clinical features requires a comprehensive description of large-scale brain activity. Flexible dynamics is key to support complex adaptive responses. In health, brain activity reconfigures over time, involving different brain areas. Brain pathologies can induce more stereotyped dynamics, which, in turn, are linked to clinical impairment. Hence, based on recent evidence that brain functional networks become more connected as ALS progresses, we hypothesized that loss of flexible dynamics in ALS would predict their clinical condition. To test this hypothesis, we quantified flexibility utilizing the functional repertoire (i.e. the number of unique patterns) expressed during the magnetoencephalography (MEG) recording, based on source-reconstructed signals. Specifically, 42 ALS patients and 42 healthy controls underwent MEG and MRI recordings. The activity of the brain areas was reconstructed in the classical frequency bands, and the functional repertoire was estimated to quantify spatio-temporal fluctuations of brain activity. In order to verify if the functional repertoire predicted disease severity, we built a multilinear model and validated it using a k-fold cross validation scheme. The comparison between the two groups revealed that ALS patients showed more stereotyped brain dynamics (P < 0.05), with reduced size of the functional repertoire. The relationship between the size of the functional repertoire and the clinical scores in the ALS group was investigated using Spearman coefficient, showing significant correlations in both the delta and the theta frequency bands. In order to prove the robustness of our results, the k-fold cross validation model was used. We found that the functional repertoire significantly predicted both clinical staging (P < 0.001 and P < 0.01, in delta and theta bands, respectively) and impairment (P < 0.001, in both delta and theta bands). In conclusion, our work shows that: 1) ALS pathology reduces the flexibility of brain dynamics; 2) sub-cortical regions play a key role in determining brain dynamics; 3) reduced brain flexibility predicts the stage of the disease as well as the severity of the symptoms. Based on these findings, our approach provides a non-invasive tool to quantify alterations in brain dynamics in ALS (and, possibly, other neurodegenerative diseases), thus opening new diagnostic opportunities as well as a framework to test disease-modifying interventions.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flexibility of brain dynamics predicts clinical impairment in Amyotrophic Lateral Sclerosis

Polverino

et al. 2022

Preprint

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“…Preprocessing and source-reconstruction was performed similarly as in Sorrentino et al 41 . In short, a 4 th -order Butterworth IIR band-pass filter was implemented, using the Fieldtrip toolbox in MATLAB 42 , in order to filter the data in the 0.5-48 Hz range.…”

Section: Methodsmentioning

confidence: 99%

“…Then, we obtained the time series of 116 regions of interest (ROIs), based on the AAL atlas 45 , using the volume conduction model proposed by Nolte 46 , and applying the Linearly Constrained Minimum Variance 47 beamformer algorithm included in the Fieldtrip toolbox 42 . The resulting time series were band-pass filtered in each canonical frequency band (i.e., delta (0.5 -4 Hz), theta (4 -8 Hz), alpha (8 -13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)). Only 90 ROIs were selected for further analysis, since we excluded ROIs related to the cerebellum because of low reliability.…”

Section: Source Reconstructionmentioning

confidence: 99%

Fading of brain network fingerprint in Parkinson’s disease predicts motor clinical impairment

Liparoti

et al. 2022

Preprint

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The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics and used to predict the cognitive decline in preclinical Alzheimer's disease. In this paper we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. Using source-reconstructed magnetoencephalography signals, we built functional connectomes and observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level). Along with previous evidence, this paper shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Liparoti

et al. 2022

Human Brain Mapping

Self Cite

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source‐reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross‐validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.