Biology subjects

Akbarian, F.

Publications and source records attributed to Akbarian, F..

3 recordsLinked to original sources

bioRxiv2023

Stimulus-related modulation in the 1/f spectral slope suggests an impaired inhibition of irrelevant information in people with multiple sclerosis

BackgroundMultiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized by neuronal and synaptic loss, resulting in an imbalance of excitatory and inhibitory synaptic transmission. MS leads to cognitive impairment such as reduced information processing speed and impaired working memory (WM). Recent studies have suggested that the 1/f slope of EEG/MEG power spectra can be associated with the excitation/inhibition (E/I) balance. A normal E/I balance is crucial for normal information processing and working memory. MethodsWe analyzed magnetoencephalographic (MEG) recordings of 38 healthy control subjects and 79 people with multiple sclerosis (pwMS) while performing an n-back working task. We computed and compared the steepness of the 1/f spectral slope through the FOOOF algorithm in the time windows [-1 0] and [0 1] s peristimulus time for both target and distractor stimulus for each brain parcel and for different working memory loads (0-back, 1-back, 2-back). ResultsThe spectral slope was significantly steeper after the stimulus onset and was correlated with reaction time. We also observed a steeper 1/f slope after distractor stimuli in healthy subjects compared to pwMS. Finally, we observed significant correlations between the 1/f spectral slope modulation and visuospatial working memory functioning in both healthy subjects and pwMS. ConclusionOur findings are consistent with an increased inhibition following stimulus onset. In pwMS, this increase is reduced, suggesting dysfunctional inhibition of irrelevant information. Finally, this impaired modulation is significantly associated with a pencil-paper test of visuospatial working memory. HighlightsO_LIThe flatter 1/f slope after distractor stimuli in people with multiple sclerosis (pwMS) compared to healthy subjects suggests a less pronounced inhibition of irrelevant information in pwMS. C_LIO_LIThe significantly flatter 1/f slope was observed in the left inferior dorsal prefrontal cortex of pwMS in both 1-back and 2-back conditions. This particular brain parcel is known for its key role in motor planning, and the maintenance of sustained attention and working memory and executive functions. C_LIO_LIA steeper 1/f slope after target and distractor stimuli suggests an increase in inhibition following stimulus onset in both healthy controls (HCs) and people with MS (pwMS). C_LIO_LIThe 1/f slope modulation correlates with visuospatial working memory performance. C_LI

neuroscience↗

bioRxiv2023

Impaired activation of the prefrontal executive network during working memory processing in multiple sclerosis

AbstractIn multiple sclerosis (MS), working memory (WM) impairment occurs soon after disease onset and significantly affects the patients quality of life. Functional imaging research in MS aims to investigate the neurophysiological underpinnings of WM impairment. In this context, we utilized a data-driven technique, the time delay embedded- hidden Markov model (TDE-HMM), to extract spectrally defined functional networks in magnetoencephalographic (MEG) data acquired during a WM visual-verbal n-back task. We observed that two networks show an altered activation in RR-MS patients. First, the activation of an early theta prefrontal network linked to stimulus encoding and attentional control significantly decreased in RR-MS compared to HC. This diminished activation correlated with reduced accuracy in task performance in the MS group, suggesting an impaired encoding and learning process. Secondly, a frontoparietal network characterized by beta coupling is activated between 300 and 600 ms after stimulus onset; this resembles the characteristic event-related P300, a cognitive marker extensively explored in EEG studies. The activation of this network is amplified in patients treated with benzodiazepine, in line with the well-known benzodiazepine-induced beta enhancement. Altogether, the TDE-HMM technique extracted task-relevant functional networks showing disease-specific and treatment- related alterations, revealing potential new markers to assess and track WM impairment in MS. HighlightsO_LIWe decomposed the brain dynamics underlying a WM n-back task in data-driven, spectrally defined whole-brain networks in both healthy controls and people with relapsing-remitting-MS (pwMS). C_LIO_LIPwMS showed a significantly decreased activation of an early theta prefrontal network linked to stimulus encoding and attentional control. C_LIO_LIThe weaker activation of this prefrontal theta network is correlated with worse task performance. C_LIO_LIA beta frontoparietal network with a P300-like temporal evolution was significantly modulated by the use of benzodiazepines. C_LIO_LIThe model distinguished disease-induced and treatment-induced dynamic network alterations. C_LI

neuroscience↗

bioRxiv2023

A novel description of the network dynamics underpinning working memory

Working memory (WM) plays a central role in cognition, prompting neuroscientists to investigate its functional and structural substrates. The WM dynamic recruits large-scale frequency-specific brain networks that unfold over a few milliseconds - this complexity challenges traditional neuroimaging analyses. In this study, we unravel the WM network dynamics in an unsupervised, data-driven way, applying the time delay embedded-hidden Markov model (TDE-HMM). We acquired MEG data from 38 healthy subjects performing an n-back working memory task. The TDE-HMM model inferred four task-specific states with each unique temporal (activation), spectral (phase-coherence connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal state performs executive functions, an alpha temporo-occipital state maintains the information, and a broad-band and spatially complex state with an M300 temporal profile leads the retrieval process and motor response. The HMM states can be straightforwardly interpreted within the neuropsychological multi-component model of WM, significantly improving the comprehensive description of WM. HighlightsO_LIWorking memory recruits different frequency-specific brain networks that wax and wane at a millisecond scale. C_LIO_LIThrough the time-delay embedded hidden (TDE-HMM) we are able to extract data-driven functional networks with unique spatial, spectral, and temporal profiles. C_LIO_LIWe demonstrate the existence of four task-specific brain networks that can be interpreted within the well-known Baddeleys multicomponent model of working memory. C_LIO_LIThis novel WM description unveils new features that will lead to a more in-depth characterization of cognitive processes in MEG data. C_LI

neuroscience↗