Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Interactive Effects of Physical Activity and APOE-ε4 on BOLD Semantic Memory Activation in Healthy Elders

Evidence suggests that physical activity (PA) is associated with the maintenance of cognitive function across the lifespan. In contrast, the apolipoproteinE-ε4 (APOE-ε4) allele, a genetic risk factor for Alzheimer\u27s disease (AD), is associated with impaired cognitive function. The objective of this study was to examine the interactive effects of PA and APOE-ε4 on brain activation during memory processing in older (ages 65–85) cognitively intact adults. A cross-sectional design was used with four groups (n = 17 each): (1) Low Risk/Low PA; (2) Low Risk/High PA; (3) High Risk/Low PA; and (4) High Risk/High PA. PA level was based on self-reported frequency and intensity. AD risk was based on presence or absence of an APOE-ε4 allele. Brain activation was measured using event-related functional magnetic resonance imaging (fMRI) while participants performed a famous name discrimination task. Brain activation subserving semantic memory processing occurred in 15 functional regions of interest. High PA and High Risk were associated with significantly greater semantic memory activation (famous\u3eunfamiliar) in 6 and 3 of the 15 regions, respectively. Significant interactions of PA and Risk were evident in 9 of 15 brain regions, with the High PA/High Risk group demonstrating greater semantic memory activation than the remaining three groups. These findings suggest that PA selectively increases memory-related brain activation in cognitively intact but genetically at-risk elders. Longitudinal studies are required to determine whether increased semantic memory processing in physically active at-risk individuals is protective against future cognitive decline

Alzheimers Disease Diagnosis using Machine Learning: A Review

Alzheimers Disease AD is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. It is a fatal brain disease that mostly affects the elderly. It steers the decline of cognitive and biological functions of the brain and shrinks the brain successively, which in turn is known as Atrophy. For an accurate diagnosis of Alzheimers disease, cutting edge methods like machine learning are essential. Recently, machine learning has gained a lot of attention and popularity in the medical industry. As the illness progresses, those with Alzheimers have a far more difficult time doing even the most basic tasks, and in the worst case, their brain completely stops functioning. A persons likelihood of having early-stage Alzheimers disease may be determined using the ML method. In this analysis, papers on Alzheimers disease diagnosis based on deep learning techniques and reinforcement learning between 2008 and 2023 found in google scholar were studied. Sixty relevant papers obtained after the search was considered for this study. These papers were analysed based on the biomarkers of AD and the machine-learning techniques used. The analysis shows that deep learning methods have an immense ability to extract features and classify AD with good accuracy. The DRL methods have not been used much in the field of image processing. The comparison results of deep learning and reinforcement learning illustrate that the scope of Deep Reinforcement Learning DRL in dementia detection needs to be explored.Comment: 10 pages and 3 figure

Lost in spatial translation - A novel tool to objectively assess spatial disorientation in Alzheimer's disease and frontotemporal dementia

Spatial disorientation is a prominent feature of early Alzheimer's disease (AD) attributed to degeneration of medial temporal and parietal brain regions, including the retrosplenial cortex (RSC). By contrast, frontotemporal dementia (FTD) syndromes show generally intact spatial orientation at presentation. However, currently no clinical tasks are routinely administered to objectively assess spatial orientation in these neurodegenerative conditions. In this study we investigated spatial orientation in 58 dementia patients and 23 healthy controls using a novel virtual supermarket task as well as voxel-based morphometry (VBM). We compared performance on this task with visual and verbal memory function, which has traditionally been used to discriminate between AD and FTD. Participants viewed a series of videos from a first person perspective travelling through a virtual supermarket and were required to maintain orientation to a starting location. Analyses revealed significantly impaired spatial orientation in AD, compared to FTD patient groups. Spatial orientation performance was found to discriminate AD and FTD patient groups to a very high degree at presentation. More importantly, integrity of the RSC was identified as a key neural correlate of orientation performance. These findings confirm the notion that i) it is feasible to assess spatial orientation objectively via our novel Supermarket task; ii) impaired orientation is a prominent feature that can be applied clinically to discriminate between AD and FTD and iii) the RSC emerges as a critical biomarker to assess spatial orientation deficits in these neurodegenerative conditions

Reduced hippocampal activation during episodic encoding in middle-aged individuals at genetic risk of Alzheimer's Disease: a cross-sectional study

BACKGROUND: The presence of the apolipoprotein E (APOE) ε4 allele is a major risk factor for the development of Alzheimer's disease (AD), and has been associated with metabolic brain changes several years before the onset of typical AD symptoms. Functional MRI (fMRI) is a brain imaging technique that has been used to demonstrate hippocampal activation during measurement of episodic encoding, but the effect of the ε4 allele on hippocampal activation has not been firmly established. METHODS: The present study examined the effects of APOE genotype on brain activation patterns in the medial temporal lobe (MTL) during an episodic encoding task using a well-characterized novel item versus familiar item contrast in cognitively normal, middle-aged (mean = 54 years) individuals who had at least one parent with AD. RESULTS: We found that ε3/4 heterozygotes displayed reduced activation in the hippocampus and MTL compared to ε3/3 homozygotes. There were no significant differences between the groups in age, education or neuropsychological functioning, suggesting that the altered brain activation seen in ε3/4 heterozygotes was not associated with impaired cognitive function. We also found that participants' ability to encode information on a neuropsychological measure of learning was associated with greater activation in the anterior MTL in the ε3/3 homozygotes, but not in the ε3/4 heterozygotes. CONCLUSION: Together with previous studies reporting reduced glucose metabolism and AD-related neuropathology, this study provides convergent validity for the idea that the MTL exhibits functional decline associated with the APOE ε4 allele. Importantly, these changes were detected in the absence of meaningful neuropsychological differences between the groups. A focus of ongoing work in this laboratory is to determine if these findings are predictive of subsequent cognitive decline

Single and Multiple Domain Amnestic MCI: two sides of the same coin?

Background. Amnestic Mild Cognitive Impairment (aMCI) is considered a transition stage between normal aging and Alzheimer's disease (AD). Two main clinical subtypes of aMCI have been identified: 1) aMCI single domain (aMCI-SD), with isolated episodic memory impairments, and 2) aMCI multiple domain (aMCI-MD), with episodic memory impairments and deficits in one or more other cognitive domains. Aims.To map the pattern of gray matter (GM) atrophy associated with aMCI-SD, aMCIMD and mild AD. Methods. A group of aMCI-SD, aMCI-MD characterized by executive function disorders, mild AD patients and cognitively unimpaired age-matched subjects underwent a comprehensive neuropsychological assessment and a high-definition MR brain scan. Voxel-based morphometry (VBM) analysis was used to characterize the GM tissue loss in each patient group, and the common pattern of GM atrophy in aMCI-SD and aMCIMD. Results. The results revealed that aMCI-SD and aMCI-MD are characterized by a common pattern of GM atrophy within the medial temporal cortex, predisposing to AD and correlating with the severity of verbal memory symptoms. Moreover, the pattern of GM atrophy observed in aMCI-SD, aMCI-MD and mild AD revealed that, from an anatomical point of view, these three clinical syndromes could represent three severity points along the continuum between normal aging and AD