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Ranasinghe, K.

Publications and source records attributed to Ranasinghe, K..

5 recordsLinked to original sources

AQuaRef: Machine learning accelerated quantum refinement of protein structures

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.

biochemistry↗

Dynamic functional connectivity MEG features of Alzheimer's disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimers disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have high sensitivity and specificity in distinguishing AD from healthy subjects. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimers disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

neuroscience↗

Cortical dynamics of speech feedback control in non-fluent Primary Progressive Aphasia

Primary Progressive Aphasia (PPA) is a clinical syndrome in which patients progressively lose speech and language abilities. The non-fluent variant of PPA (nfvPPA) is characterised by impaired motor speech and agrammatism. To date, no study in nfvPPA patients has either examined speech motor control behaviour or imaged the speech motor control network during vocal production. Here, we did this using a novel structure-function imaging approach integrating magnetoencephalographic imaging of neural oscillations with voxel-based morphometry (VBM). We examined task-induced non-phase-locked neural oscillatory activity during a vocal motor control task, where participants were prompted to phonate the vowel /{square}/ for [~]2.4s while the pitch of their auditory feedback was shifted either up or down by 100 cents for a period of 400ms mid-utterance. Participants were 18 nfvPPA patients (14 female, mean age = 67.79 {+/-} 8.02 years) and 17 controls (13 female, mean age = 64.81 {+/-} 5.76 years). Patients showed a smaller compensation response to pitch perturbation than controls (p < 0.05). Task-induced neural oscillations across five frequency bands were reconstructed in source space for each subject during pitch feedback perturbation. Patients exhibited reduced task-induced alpha-band (8-12Hz) neural activity unrelated to their atrophy patterns, in the right temporal lobe and the right temporoparietal junction (p < 0.01) from 250ms to 750ms after pitch perturbation onset. Patients also showed increased task-induced beta-band (12-30Hz) activity also unrelated to cortical atrophy in the left dorsal sensorimotor cortex, left premotor cortex and the left supplementary motor area (p < 0.01) from 50ms to 150ms after pitch perturbation onset. Reduced average alpha-band power at the peak voxel in the temporoparietal cluster in the right hemisphere could predict speech motor impairment in patients ({beta} = 3.41, F = 8.31, p = 0.0128) whereas increased average beta-band power at the peak voxel in the left dorsal sensorimotor cluster could not ({beta} = -1.75, F = 1.72, p = 0.2123). Collectively, these results suggest significant disruption in sensorimotor integration during vocal production in nfvPPA patients which occurs unrelated to patterns of atrophy. These findings highlight how multimodal structure-function imaging in PPA enhances our understanding of its pathophysiological sequelae.

neuroscience↗

Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease

BackgroundNeuronal and circuit level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (A{beta}) in preclinical models of Alzheimers disease (AD). These relationships remain poorly understood in patients with AD. MethodsUsing empirical spectra from magnetoencephalography (MEG) and computational modeling (neural mass model; NMM) we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and A{beta}, measured by positron emission tomography (PET), in patients with AD. ResultsPatients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher A{beta} depositions. ConclusionsOur results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and A{beta} in patients with AD. FundingThis study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM & GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimers Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL and (KGR); 2019-A-013-SUP (KGR); a grant from the Alzheimers Association: (PCTRB-13-288476) (KAV), and made possible by Part the CloudTM, (ETAC-09-133596); a grant from Tau Consortium (GDR & WJJ), and a gift from the S. D. Bechtel Jr. Foundation.

neuroscience↗

Temporal specificity of abnormal neural oscillations during phonatory events in Laryngeal Dystonia

Laryngeal Dystonia is a debilitating disorder of voicing in which the laryngeal muscles are intermittently in spasm resulting in involuntary interruptions during speech. The central pathophysiology of laryngeal dystonia, underlying computational impairments in vocal motor control, remains poorly understood. Although prior imaging studies have found aberrant activity in the central nervous system during phonation in patients with laryngeal dystonia, it is not known at what timepoints during phonation these abnormalities emerge and what function may be impaired. To investigate this question, we recruited 22 adductor laryngeal dystonia patients (15 female, age range = 28.83-72.46 years) and 18 controls (8 female, age range = 27.40-71.34 years). We leveraged the fine temporal resolution of magnetoencephalography to monitor neural activity around glottal movement onset, subsequent voice onset and after the onset of pitch feedback perturbations. We examined event-related beta-band (12-30 Hz) and high-gamma band (65-150 Hz) neural oscillations. Prior to glottal movement onset, we observed abnormal frontoparietal motor preparatory activity. After glottal movement onset, we observed abnormal activity in somatosensory cortex persisting through voice onset. Prior to voice onset and continuing after, we also observed abnormal activity in the auditory cortex and the cerebellum. After pitch feedback perturbation onset, we observed no differences between controls and patients in their behavioural responses to the perturbation. But in patients, we did find abnormal activity in brain regions thought to be involved in the auditory feedback control of vocal pitch (premotor, motor, somatosensory and auditory cortices). Our study results confirm the abnormal processing of somatosensory feedback that has been seen in other studies. However, there were several remarkable findings in our study. First, patients have impaired vocal motor activity even before glottal movement onset, suggesting abnormal movement preparation. These results are significant because: (i) they occur before movement onset, abnormalities in patients cannot be ascribed to deficits in vocal performance, and (ii) they show that neural abnormalities in laryngeal dystonia are more than just abnormal responses to sensory feedback during phonation as has been hypothesised in some previous studies. Second, abnormal auditory cortical activity in patients begins even before voice onset, suggesting abnormalities in setting up auditory predictions before the arrival of auditory feedback at voice onset. Generally, activation abnormalities identified in key brain regions within the speech motor network around various phonation events not only provide temporal specificity to neuroimaging phenotypes in laryngeal dystonia but also may serve as potential therapeutic targets for neuromodulation.

neuroscience↗