Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

Orai2 down-regulation potentiates soce and decreases a\u3b242 accumulation in human neuroglioma cells

Senile plaques, the hallmarks of Alzheimer's Disease (AD), are generated by the deposition of amyloid-beta (A\u3b2), the proteolytic product of amyloid precursor protein (APP), by \u3b2 and \u3b3-secretase. A large body of evidence points towards a role for Ca2+ imbalances in the pathophysiology of both sporadic and familial forms of AD (FAD). A reduction in store-operated Ca2+ entry (SOCE) is shared by numerous FAD-linked mutations, and SOCE is involved in A\u3b2 accumulation in different model cells. In neurons, both the role and components of SOCE remain quite obscure, whereas in astrocytes, SOCE controls their Ca2+-based excitability and communication to neurons. Glial cells are also directly involved in A\u3b2 production and clearance. Here, we focus on the role of ORAI2, a key SOCE component, in modulating SOCE in the human neuroglioma cell line H4. We show that ORAI2 overexpression reduces both SOCE level and stores Ca2+ content, while ORAI2 downregulation significantly increases SOCE amplitude without affecting store Ca2+ handling. In A\u3b2-secreting H4-APPswe cells, SOCE inhibition by BTP2 and SOCE augmentation by ORAI2 downregulation respectively increases and decreases A\u3b242 accumulation. Based on these findings, we suggest ORAI2 downregulation as a potential tool to rescue defective SOCE in AD, while preventing plaque formation

Early detection of Alzheimer’s disease from cortical and hippocampal local field potentials using an ensembled machine learning model

Early diagnosis of Alzheimer’s disease (AD) is a very challenging problem and has been attempted through data-driven methods in recent years. However, considering the inherent complexity in decoding higher cognitive functions from spontaneous neuronal signals, these data-driven methods benefit from the incorporation of multimodal data. This work proposes an ensembled machine learning model with explainability (EXML) to detect subtle patterns in cortical and hippocampal local field potential signals (LFPs) that can be considered as a potential marker for AD in the early stage of the disease. The LFPs acquired from healthy and two types of AD animal models (n=10 each) using linear multielectrode probes were endorsed by electrocardiogram and respiration signals for their veracity. Feature sets were generated from LFPs in temporal, spatial and spectral domains and were fed into selected machine-learning models for each domain. Using late fusion, the EXML model achieved an overall accuracy of 99.4%. This provided insights into the amyloid plaque deposition process as early as 3 months of the disease onset by identifying the subtle patterns in the network activities. Lastly, the individual and ensemble models were found to be robust when evaluated by randomly masking channels to mimic the presence of artefacts

IONIC CURRENTS IN HAIR CELLS DISSOCIATED FROM FROG SEMICIRCULAR CANALS AFTER PRECONDITIONING UNDER MICROGRAVITY CONDITIONS.

American Journal of Physiology - Section "Regul Integr Comp Physiol

Ionic currents in hair cells dissociated from frog semicircular canals after preconditioning under microgravity conditions.

The effects of microgravity on the biophysical properties of frog labyrinthine hair cells have been examined by analyzing calcium and potassium currents in isolated cells by the patch-clamp technique. The entire, anesthetized frog was exposed to vector-free gravity in a random positioning machine (RPM) and the functional modification induced on single hair cells, dissected from the crista ampullaris, were subsequently studied in vitro. The major targets of microgravity exposure were the calcium/potassium current system and the kinetic mechanism of the fast transient potassium current, I(A). The amplitude of I(Ca) was significantly reduced in microgravity-conditioned cells. The delayed current, I(KD) (a complex of I(KV) and I(KCa)), was drastically reduced, mostly in its I(KCa) component. Microgravity also affected I(KD) kinetics by shifting the steady-state inactivation curve toward negative potentials and increasing the sensitivity of inactivation removal to voltage. As concerns the I(A), the I-V and steady-state inactivation curves were indistinguishable under normogravity or microgravity conditions; conversely, I(A) decay systematically displayed a two-exponential time course and longer time constants in microgravity, thus potentially providing a larger K(+) charge; furthermore, I(A) inactivation removal at -70 mV was slowed down. Stimulation in the RPM machine under normogravity conditions resulted in minor effects on I(KD) and, occasionally, incomplete I(A) inactivation at -40 mV. Reduced calcium influx and increased K(+) repolarizing charge, to variable extents depending on the history of membrane potential, constitute a likely cause for the failure in the afferent mEPSP discharge at the cytoneural junction observed in the intact labyrinth after microgravity conditioning

Dampened slow oscillation connectivity anticipates amyloid deposition in the PS2APP mouse model of Alzheimer’s disease

To fight Alzheimer’s disease (AD), we should know when, where, and how brain network dysfunctions initiate. In AD mouse models, relevant information can be derived from brain electrical activity. With a multi-site linear probe, we recorded local field potentials simultaneously at the posterior-parietal cortex and hippocampus of wild-type and double transgenic AD mice, under anesthesia. We focused on PS2APP (B6.152H) mice carrying both presenilin-2 (PS2) and amyloid precursor protein (APP) mutations, at three and six months of age, before and after plaque deposition respectively. To highlight defects linked to either the PS2 or APP mutation, we included in the analysis age-matched PS2.30H and APP-Swedish mice, carrying each of the mutations individually. Our study also included PSEN2−/− mice. At three months, only predeposition B6.152H mice show a reduction in the functional connectivity of slow oscillations (SO) and in the power ratio between SO and delta waves. At six months, plaque-seeding B6.152H mice undergo a worsening of the low/high frequency power imbalance and show a massive loss of cortico-hippocampal phase-amplitude coupling (PAC) between SO and higher frequencies, a feature shared with amyloid-free PS2.30H mice. We conclude that the PS2 mutation is sufficient to impair SO PAC and accelerate network dysfunctions in amyloid-accumulating mice