Impairment Classification Research Articles

Can scuba diving transform the lives of people with physical impairments? A mixed methods study

PurposeThis study aimed to: (1) test and explain the type of experience scuba diving is among people with physical impairments based on the experience-type framework; (2) assess and describe their personality based on the Big Five domains; and (3) identify if personality, years diving, and diving level predict experience-type.MethodsAn explanatory sequential mixed methods design was employed. The quantitative phase used a cross-sectional survey (n = 103). The qualitative phase used follow-up interviews with 15 participants divided into 3 case study groups. Joint displays with meta-inferences integrated the data.ResultsQuantitative and qualitative findings concurred on scuba being a transformative experience. 82.52% of survey participants reported scuba as a transformative experience, with no significant differences on experience impact based on impairment category (p = 0.56), impairment onset (p = 0.66), gender (p = 0.08), race/ethnicity (p = 0.51), or age (p = 0.07). Big Five personality domains, years diving, or diving level did not predict experience impact (R2 = 0.14, F(12,90) = 1.304, p = 0.2305). Data strand results differed on salient personality domains. Seven qualitative themes emerged, five on experience-type and two on personality.ConclusionsWe recommend the exploration of scuba diving as a prospective rehabilitation intervention.

Sensitivity of Individual and Composite Test Scores from the Cogstate Brief Battery to Mild Cognitive Impairment and Dementia Due to Alzheimer's Disease.

The Cogstate Brief Battery (CBB) is a computerized cognitive test battery used commonly to identify cognitive deficits related to Alzheimer's disease (AD). However, AD and normative samples used to understand the sensitivity of the CBB to AD in the clinic have been limited, as have the outcome measures studied. This study investigated the sensitivity of CBB outcomes, including potential composite scores, to cognitive impairment in mild cognitive impairment (MCI) and dementia due to AD, in carefully selected samples. Samples consisted of 4,871 cognitively unimpaired adults and 184 adults who met clinical criteria for MCI (Clinical Dementia Rating (CDR) = 0.5) or dementia (CDR > 0.5) due to AD and CBB naive. Speed and accuracy measures from each test were examined, and theoretically- and statistically-derived composites were created. Sensitivity and specificity of classification of cognitive impairment were compared between outcomes. Individual CBB measures of learning and working memory showed high discriminability for AD-related cognitive impairment for CDR 0.5 (AUCs ∼ 0.79-0.88), and CDR > 0.5 (AUCs ∼ 0.89-0.96) groups. Discrimination ability for theoretically derived CBB composite measures was high, particularly for the Learning and Working Memory (LWM) composite (CDR 0.5 AUC = 0.90, CDR > 0.5 AUC = 0.97). As expected, statistically optimized linear composite measures showed strong discrimination abilities albeit similar to the LWM composite. In older adults, the CBB is effective for discriminating cognitive impairment due to MCI or AD-dementia from unimpaired cognition with the LWM composite providing the strongest sensitivity.

Occurrence of injuries and illnesses in athletes with a physical impairment; a forty-week prospective cohort study

ObjectivesCurrently, the knowledge about the epidemiology of sport-related health problems of athletes with a physical impairment is limited. Therefore, this study aims to describe the prevalence, incidence and severity of sport-related health problems of athletes with a physical impairment in the Netherlands over a one sport season. DesignProspective cohort study. MethodsAthletes (n = 99) with a physical impairment participated in this study. After completing a baseline questionnaire, athletes started a 40-week health monitoring period, during which they completed the Dutch translation of the OSTRC Questionnaire on Health Problems every week. Based on the reported information, injury and illness prevalence, incidence, weekly cumulative severity score, weekly time loss from sport and the total burden were calculated. ResultsA total of 368 health problems were reported by 95 athletes. These health problems consisted of 258 injuries and 110 illnesses. The average weekly prevalence of health problems was 48.0 %; for injuries 34.6 %, and illnesses 13.4 %. Differences were found when considering injury severity, with the highest severity scores for athletes with a limb deficiency. Furthermore, recreational athletes reported the highest injury severity scores, followed by athletes who competed nationally and athletes who competed internationally. ConclusionsData from this study show a high average weekly prevalence of health problems, and differences amongst various impairment categories and sport levels when considering the severity of injuries. These results add to the knowledge regarding sport-related health problem epidemiology in this population, indicating that impairment category and sport level should be taken into account when developing injury prevention strategies.

Plasma biomarkers, memory impairment, and imaging measures of Alzheimer’s disease in American Indians

AbstractBackgroundAlzheimer’s disease (AD) is best defined in vivo using quantifiable, objective markers reflecting amyloid deposition (A), pathologic tau (T), and neurodegeneration (N). However, gold standards for ATN (imaging or cerebrospinal proteins) are expensive, invasive, and/or impractical in low resource settings. Circulating proteins offer a practical alternative. Candidate plasma markers include amyloid‐β (Aβ40&42), phosphorylated tau‐181 (ptau), glial fibrillary protein (GFAP), and neurofilament light chain (NfL). However, no such markers have been evaluated in American Indians.MethodThe Strong Heart Study recruited 401 American Indians over 3 states in 2017‐2019 for MRI, cognitive testing, and clinical examination. MRI included T1 sequences, which were processed using FreeSurfer to quantify structural volumes. Neuropsychological testing incuded California Verbal Learning Test, which was operationalized into categories of memory impairment using indices of learning and recall. Plasma samples were sent to University of Gothenburg for Simoa Quanterix assay. Statistics included descriptive summary; multivariate regression; and receiver operating characteristic analysis.ResultThe study population was mean age 78 (SD 4.7, range 70‐95); majority female (71%); and 21% APOE ε4 allele carriers. An estimated 31% had any memory impairment (13.8% retrieval, 6.5% storage, 10.7% encoding). Median plasma marker values were: ptau 5.0pg/mL (range: 1.5‐442), Aβ40 128pg/mL (116‐175), Aβ42 8.2pg/mL (6.7‐9.7), GFAP 150pg/mL (0‐651), and NfL 31.6pg/mL (9.7‐343); median Aβ42:40 ratio 0.06. Comparing APOEε4 carriers to non‐carriers (Table1), ptau was +12%, Aβ40 ‐0.8%, Aβ42 ‐8.4%, Aβ4240 ‐16.7% (P = 0.006), GFAP +16.1%, NfL +3.2%, but only Aβ4240 was significant. Linear regressions with brain volumes identified significant associations for Aβ42, Aβ40, GFAP, and NfL (but not ptau) with ventricle, cortical, and overall volume; NfL was also associated with entorhinal and hippocampal volume. GFAP and ptau were significantly higher in those with memory encoding deficit; NfL higher with encoding or retrieval deficit (Table2). Although not significant, Aβ40 was lower with storage deficit, and Aβ42 with encoding or storage deficit. AUC range for plasma markers to discriminate memory impaired from intact: 0.4‐0.6.ConclusionThese data are a first look at ATN plasma markers in a population‐based cohort of American Indians, an underrepresented group in AD research. Future research should address generalizability and measurement validity.

Comparing Clinical Diagnosis of Domains of Impairment in individuals with Mild Cognitive Impairment with the NIH Toolbox‐Cognitive Battery Performance

AbstractBackgroundMild Cognitive Impairment (MCI) represents the phase between age‐associated cognitive decline and Alzheimer’s disease and related dementias. Examination of these individuals may be especially important through computer‐based batteries such as the NIH Toolbox‐Cognition Battery (NIHTB‐CB). To inform NIHTB‐CB validity, we compared MCI patients’ performance against categories of cognitive impairment defined by the consensus‐based clinical diagnosis form of the National Alzheimer’s Disease Coordinating Center (NACC) Unified Data Set UDS3. We expected a correspondence between MCI consensus‐based clinical deficit areas and specific NIHTB‐CB task performance.MethodRetrospective data analysis was conducted on 65 MCI participants from the Michigan Alzheimer’s Disease Research Center, with a mean age of 74±8.3, 65% female, who completed UDS3 and NIHTB‐CB measures. The UDS3 forms consist of interviews, medical/neurological examinations, and neuropsychological testing, with a clinical consensus‐based diagnosis form listing domains of cognitive impairment representing memory, language, attention, executive, and visuospatial functioning. Four domain impairment groupings with a minimum of 10 participants each were established (Executive Functioning, n = 10; Memory, n = 27; Memory+Executive Functioning, n = 17; and Memory+Language n = 11) and compared to NIHTH‐CB performance. ANOVAs adjusted for demographics and post hoc tests were conducted to differentiate across clinical consensus categories for the MCI participants.ResultTwo NIHTB‐CB measures yielded significant differences across the clinical grouping: Picture Sequence Memory (PSM, F = 3.3, p = 0.03) and Pattern Comparison (PC, F = 4.1, p = 0.01). For PSM, post hoc tests demonstrated higher scores for the Executive compared to Memory+Executive (PSM: p<0.02) groups with a trend for Executive versus Memory groups (p<.07). For PC, the Memory group performed better than both the Memory+Executive (p<.02) and Memory+Language (p<.04).ConclusionThe better memory performance on PSM of the MCI Executive group compared to the two groups with memory impairment is consistent with expectation. PC, as a measure of executive ability and processing speed was sensitive to clinically diagnosed executive impairment. Lower scores for the Memory+Language group on PC were unexpected. Efficient language functioning may be necessary for complex pattern comparison as dimensions shift. Findings support the validity of NIHTB‐CB. They also suggest the need for additional research on language demands for executive measures.

Detection of Early Alzheimer’s disease at AdventHealth: A Davos Alzheimer’s Collaborative flagship site

AbstractBackgroundEarly identification of Alzheimer’s disease (AD) is critical for disease‐modifying therapies. The Davos Alzheimer’s Collaborative flagship program tested the feasibility of implementing a digital cognitive assessment (DCA) followed by a blood biomarker (BBM) for early detection of cognitive impairment (CI).MethodIndividuals ≥65 years without dementia were approached via their primary care provider (PCP) or through direct‐to‐consumer (DTC) social media. After consenting, participants completed the Cogstate Brief Battery (CBB) DCA. Participants with an abnormal or borderline CBB score were offered the Montreal Cognitive Assessment (MoCA) and the PrecivityAD® blood test, a CLIA‐certified laboratory developed test that uses mass spectrometry to analyze biomarkers to identify brain amyloid plaques (reported by the Amyloid Probability Score‐APS) in individuals with CI.ResultOver 2300 participants expressed interest. Of 2001 eligible, 1076 (96% social media, 4% PCP) e‐consented. 742 completed the CBB of which 211 (28%) were borderline, 113 (15%) abnormal, and 418 (56%) were negative for CI. Of the 324 with borderline or abnormal CBB scores, 219 (67%) completed the MoCA (59% Normal range, 38% Mild CI range, 2% Moderate CI range, and 0.5% Severe CI range). Of the 324, 218 (67%) received BBM: 18.8% had High APS, 67.9% Low APS, and 13.3% Intermediate (e.g. non‐informative) APS. The APS result for participants with a normal MoCA showed 20% with High APS, 12% with an Intermediate APS, and 69% with Low APS. Of those with an impaired MoCA 18% had High APS, 15% Intermediate and 67% low APS. A Fisher’s Exact test determined there was no statistically significant relationship between MoCA impairment and APS category (p‐value 0.68).ConclusionThis study highlights the success of a DTC approach. The MoCA, alone, is insufficient to identify risk of AD in individuals with CI. A more comprehensive clinical evaluation of AD can be enhanced with the addition of a BBM leading to better disease‐modifying strategies.

Amygdala tau deposition best discriminates between stages of memory impairment

AbstractBackgroundThe recent development of positron emission tomography (PET) radiotracers for tau aggregates allows quantifying the regional tau burden in‐vivo. We aimed to investigate the associations between regional tauopathy and episodic memory outcomes in the AD spectrum from clinically normal to the dementia stage.MethodWe recruited 111 participants between 2019 and 2023 at the Memory Clinic of Saint‐Luc University Hospital. Each participant underwent [F18]‐MK6240 tau‐PET, 3DT1 MRI, cognitive assessment, and either lumbar puncture or [F18]‐Flutemetamol PET for amyloid status. Tau burden was quantified using Freesurfer by referring to the Desikan‐Kiliany lobe cortical parcellation, as well as in several medio‐temporal regions (MTL; i.e., entorhinal cortex, amygdala, hippocampus). Memory was assessed using three Z‐scores from the FCSRT: Sum of Free Recall (SFR), Sum of Total Recall (STR), and Sum of Delayed Free Recall (DFR). The average of these scores forms the Memory Composite Score (MCS). Participants were classified in three groups (Table 1): amyloid‐negative clinically normal participants (n = 44); non‐demented participants with AD pathology (early AD, n = 45); and AD dementia participants (n = 22). Based on their SFR and STR scores, subjects were also classified as having retrieval and/or encoding memory deficits according to a Stage of Objective Memory Impairment (SOMI) classification, inspired from Grober and colleagues (2018). Analyses were corrected for multiple comparisons.ResultIn early AD, SFR and DFR correlated with tauopathy in the bilateral entorhinal cortex (EC) and amygdala (Fig.1). MCS correlated with tauopathy in the bilateral amygdala, and the right frontal lobe. Tau levels did not correlate with STR in any regions. Moreover, MTL tauopathy differed between SOMI stages: Specifically, amygdala tauopathy gradually increased with retrieval deficits (SOMI1‐2) and encoding deficits (SOMI3‐4). In contrast, tauopathy in the EC, the temporal lobe, and the parietal lobe was elevated in all participants with memory deficits, but did not distinguish between retrieval and encoding deficits (Table 2).ConclusionTauopathy in the MTL ROIs is predictive of retrieval deficits in early stage of AD. SFR is a more sensitive measure of this early decline compared to STR. Amygdala tauopathy differs between patients with retrieval and encoding deficits.

Clinical Manifestations.

Racial and ethnic disparities in ADRD diagnosis are well documented but remain poorly understood. Clinical ADRD diagnosis relies on neuropsychological assessment, which is interpreted by comparing individual performance to demographically adjusted reference group norms. Differential use of demographic adjustment procedures and sources of normative data may compromise diagnostic accuracy of neuropsychological findings in diverse individuals. Using two different demographic adjustment approaches derived from two distinct normative samples, we generated cognitive impairment categories and evaluated their associations with longitudinal trajectories of independent executive functioning and memory scores. Participants were 717 older adults (age: 77.5±6.9, 56% women) enrolled in the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study who completed up to 3 annual visits. The main predictor was cognitive impairment category (no impairment, single-domain, multiple-domain) based on Uniform Data Set Neuropsychological Battery (UDSNB) scores at baseline. UDSNB scores were either demographically unadjusted or adjusted (age, sex, education, and race/ethnicity) using data from the National Alzheimer's Coordinating Center (NACC; N = 8313, 16% Black, 84% White) or KHANDLE (N = 397, 26% Asian, 18% Black, 30% LatinX, 26% White) samples. Linear mixed effect models were used to examine the associations between differentially derived cognitive impairment categories with longitudinal changes on episodic memory and executive functioning composites from an independent cognitive battery (Spanish and English Neuropsychological Assessment Scales, SENAS). Cognitive impairment categories derived from the KHANDLE sample were more strongly associated in all four racial and ethnic groups with longitudinal decline on episodic memory compared to the NACC sample-derived categories (Figure 1). Similar, albeit attenuated, effects were observed in relation to longitudinal decline on the executive function composite. There were no clear differences in the associations between cognitive impairment categories with executive functioning or memory decline using demographically adjusted vs. unadjusted scores. Our results highlight the need for greater representation of diverse individuals in normative samples underlining the limitations of the NACC-derived normative algorithms for cognitive assessment of individuals from diverse populations. Further investigations into the clinical value of the practice of demographic adjustments are also warranted.

A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson's Disease for Use in Tele-Assessments.

Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extremity (finger tapping, hand movements, and pronation-supination movements of the hands) and lower extremity (toe tapping and leg agility) tasks consistent with the MDS-UPDRS. Overall, we found a 12.2% improvement in accuracy after expanding the training dataset and pre-training using max-vote inference on hand movement tasks. Moreover, we found that the classification performance improves for every task except toe tapping after the addition of HOA training data. These findings suggest that learning from HOA motion data can implicitly improve the representations of PD motion data for the purposes of motor impairment classification. Further, our results suggest that unsupervised pre-training can improve the performance of motor impairment classifiers without any additional annotated PD data, which may provide a viable solution for a widely deployable telemedicine solution.

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.

Should we enlarge the indication for kidney biopsy in patients with diabetes? The pro part.

Diabetic nephropathy (DN) and non-diabetic renal diseases (NDRD) represent intricate challenges in diagnosis and treatment within the context of the global diabetes epidemic. As the prevalence of diabetes continues to escalate, effective management of renal complications becomes paramount. Recent advancements in comprehending the multifaceted nature of renal damage, fueled by insights from histopathological investigations, offer unprecedented prospects for refining diagnostic strategies and customizing therapeutic interventions. Renal biopsies have emerged as indispensable tools for unraveling the diverse phenotypes of renal damage in diabetes. The pioneering study by Mazzucco identified three classes of renal damage in type 2 diabetes patients: classical diabetic glomerulosclerosis (DN), vascular and ischemic glomerular changes (NDRD), and other glomerulonephritides in the presence (DN+NDRD, mixed forms) or absence of DN (NDRD). The prevalence of these classes varies widely in published studies, influenced by factors such as ethnicity, geography and selection criteria for renal biopsy. Moreover, the international Renal Pathology Society consensus classification system has stratified the classical diabetic nephropathy into progressive categories of renal impairment, a breakthrough that aids in prognostication. Histopathological scrutiny, particularly the intricate correlation between glomerular and tubulointerstitial lesions, contributes profoundly to enhancing our grasp of the phenotype's heterogeneity. This amplified comprehension holds the potential to steer personalized treatment strategies. Cutting-edge interventions, encompassing sodium-glucose cotransporter 2 inhibitors, mineralocorticoid receptor antagonists and anti-endothelin receptor agents, are broadening the arsenal against renal injury in diabetes. When combined with the profound insights garnered from histopathological, omics, imaging and clinical data, these therapeutic avenues promise a transformative shift towards precision-driven care paradigms. Collaborative efforts uniting researchers, clinicians and patients are indispensable for propelling our knowledge of diabetic renal damage and ameliorating patient outcomes. The fusion of histopathological, omics and imaging findings into clinical decision-making harbors the potential to customize interventions and optimize care for individuals grappling with diabetes-associated renal complications. Furthermore, groundbreaking initiatives like the iBeat Study within the BEAt-DKD (Biomarker Enterprise to Attack Diabetic Kidney Disease) project (https://www.beat-dkd.eu/), elucidating distinct phenotypes of renal damage within diabetes, underscore the imperative necessity of integrating histopathological data into the broader framework of diabetic renal management.

Childhood cerebral visual impairment subtype classification based on an extensive versus a limited test battery.

To classify CVI subtypes and compare the added value of an extensive test battery over a limited test battery in subtype classification of cerebral visual impairment (CVI) in children. Seventy-five children with a clinical diagnosis of CVI (median [IQR] age: 9 [7-12] years) were identified from the medical records. The extensive test battery included visual acuity, contrast sensitivity, ocular alignment, eye movement analysis, visual field analysis, optic nerve head evaluation, and evaluation of visual perception. The limited test battery included visual acuity, contrast sensitivity, ocular alignment, and evaluation of visual perception. Principal component analysis (PCA) followed by cluster analysis was done, for both test batteries separately, to determine the optimum subtype classification for CVI. Fifty-one participants with an extensive test battery with mild to moderate visual impairment were included in the main analysis. This resulted in four CVI subtypes for the extensive test battery (subtle characteristics, higher-level visual function deficits, lower-level visual function deficits, and higher- and lower- level visual function deficits) and three CVI subtypes for the limited test battery (subtle characteristics, higher-level visual function deficits, and higher- and lower- level visual function deficits). There were significant differences between the subtypes for 9 out of 10 measures of the extensive and all 4 measures of the limited test battery (p < 0.05). The subtle characteristics subtype (extensive n = 19, limited n = 15) showed near normal lower and higher-level visual functions in both test batteries. The higher-level visual function deficits subtype (extensive n = 18, limited n = 24) showed near normal visual acuity combined with significant visual perceptual deficits in both test batteries; accompanied by visual pathways defects and abnormal eye movement behavior in the extensive test battery. The higher- and lower- level visual function deficits subtype (extensive n = 4, limited n = 12) showed both higher and lower-level visual function deficits in both test batteries, but application of the extensive test battery revealed additional visual pathways defects and abnormal eye movement behavior. The lower-level visual function deficits CVI subtype (extensive n = 10) was a new subtype identified by the extensive test battery. This subtype showed lower-level visual function deficits together with abnormal eye movement measures. This data-driven study has provided meaningful CVI subtype classifications based on the outcomes of various key functional and structural measures in CVI diagnosis. Comparison of the extensive test battery to the limited test battery revealed the added value of an extensive test battery in classifying CVI. The outcomes of this study, therefore, have provided a new direction in the area of CVI classification.

Procedural auditory category learning is selectively disrupted in developmental language disorder.

Speech communication depends on accurate perception and identification of speech sounds, which vary across talkers and word or sentence contexts. The ability to map this variable input onto discrete speech sound representations relies on categorization. Recent research and theoretical models implicate the procedural learning system in the ability to learn novel speech and non-speech categories. This connection is particularly intriguing because several language disorders that demonstrate linguistic impairments are proposed to stem from procedural learning and memory dysfunction. One such disorder, Developmental Language Disorder (DLD), affects 7.5% of children and persists into adulthood. While DLD is associated with general linguistic impairments, it is not yet clear how fundamental perceptual and cognitive processes supporting language are impacted, such as the ability to learn novel auditory categories. We examined auditory category learning in children with DLD and typically developed (TD) children using two well-matched nonspeech auditory category learning challenges to draw upon presumed procedural (information-integration) versus declarative (rule-based) learning systems. We observed impaired information-integration category learning and intact rule-based category learning in the DLD group. Quantitative model-based analyses revealed reduced use of, and slower shifting to, optimal procedural-based strategies in DLD and slower shifting to but similarly efficient use of optimal hypothesis-testing strategies. The dissociation is consistent with the Procedural Deficit Hypothesis of language disorders and supports the theoretical distinction of multiple category learning systems. These findings demonstrate that highly controlled experimental tasks assessing perceptual and cognitive abilities can relate to real-world challenges facing individuals with DLD in forming stable linguistic representations.

A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data.

Toward Removing Barriers in the Evaluation, Diagnosis, and Care of Individuals With Cerebral Visual Impairments

Cerebral visual impairment (CVI) is a leading cause of pediatric visual impairment worldwide. Despite the ramifications on multiple developmental domains and quality of life factors, substantial barriers remain for evaluation, diagnosis, treatment, and education of this growing population of children and adults. At the root is a lack of consensus on the definition and diagnostic criteria of CVI and a narrow ocular/acuity-based classification of visual impairment. This article covers four main issues that need to be urgently addressed to make strides toward removing current barriers. These challenges highlight the need for policy change at multiple levels to help ensure that individuals with CVI will have access to appropriate (re)habilitation and education services regardless of their acuity or visual function. Suggested solutions include the implementation of a globally accepted definition of CVI and increased multidisciplinary training programs. These will support international efforts to develop and implement quantitative interdisciplinary assessment methods, identify meaningful and effective intervention approaches, and monitor long-term outcomes for those with CVI. Additionally, sensitive and specific markers can be identified for differentiating CVI from other related developmental conditions, such as autism spectrum disorder and developmental coordination disorder. Ultimately, these proposed changes will lead toward improved quality of life for individuals with CVI and their caregivers.

Early rise of glutamate-glutamine levels in mild cognitive impairment: Evidence for emerging excitotoxicity

BackgroundUse proton magnetic resonance spectroscopy (1H-MRS) non invasive technique to assess the modifications of glutamate-glutamine (Glx) and gammaaminobutyric acid (GABA) brain levels in patients reporting a cognitive complain MethodsPosterior cingular cortex 1H-MRS spectra of 46 patients (19 male, 27 female) aged 57 to 87 years (mean : 73.32 ± 7.33 years) with a cognitive complaint were examined with a MEGA PRESS sequence at 3T, and compounds Glutamateglutamine (Glx), GABA, Creatine (Cr) and NAA were measured. From this data the metabolite ratios Glx/Cr, GABA/Cr and NAA/Cr were calculated. In addition, all patient performed the Mini Mental State Evaluation (MMSE) and 2 groups were realized with the clinical threshold of 24. Results16 patients with MMSE 〈 24 and 30 patients with MMSE 〉 24. Significant increase of Glx/Cr in PCC of patients with MMSE 〈 24 compared to patients with MMSE 〉 24. Moreover, GABA/Cr ratio exhibited a trend for a decrease in PCC between the two groups, while they showed a significant decrease NAA/Cr ratio. ConclusionOur results concerning Glx are in agreement with a physiopathological hypothesis involving a biphasic variation of glutamate levels associated with excitotoxicity, correlated with the clinical evolution of the disease. These observations suggest that MRS assessment of glutamate levels could be helpful for both diagnosis and classification of cognitive impairment in stage.

The Mini-Mental State Examination (MMSE) to Measure the Cognitive Status of Retired Teachers

The purpose of this study was to describe the cognitive status of teachers who are still actively teaching and who have retired. The research method used is descriptive quantitative. The research site is SMP N 1 Bantul. Research respondents are teachers who are still teaching at SMP N 1 Bantul and who have retired from SMP N 1 Bantul. The technique of determining the sample using random. This research instrument uses the MMSE that has been provided. The result of this study is to have an overview of the distribution of teacher respondents based on cognitive status who are still actively teaching and have retired that the largest number is retired with the definitive category of cognitive impairment as many as 30 people (41%), the category of possible cognitive impairment as many as 19 people (26%) , and the normal category as many as 25 people (34%).

Walking path images from real-time location data predict degree of cognitive impairment

BackgroundWe propose a novel approach that uses spatial walking patterns produced by real-time location systems to classify the severity of cognitive impairment (CI) among residents of a memory care unit. MethodsEach participant was classified as “No-CI”, “Mild-Moderate CI” or “Severe CI” based on their Mini-Mental State Examination scores. The location data was distributed into windows of various durations (5, 10, 15 and 30 min) and transformed into images used to train a custom convolutional neural network (CNN) at each window size. Class Activation Mapping was applied to the top-performing models to determine the features of images associated with each class. ResultsThe best performing model achieved an accuracy of 87.38 % (30-min window length) with an overall pattern that larger window sizes perform better. The class activation maps were effectively consolidated into a Cognitive Impairment Classification Value (CICV) score that distinguishes between No-CI, Mild-Moderate CI, and Severe CI. ConclusionThe class activation maps show that the CNN made relevant and intuitive distinctions for paths corresponding to each class. Future work should validate the proposed techniques with participants who are well-characterized clinically, over larger and diversified settings, and towards classification of neuropsychiatric symptoms such as motor agitation, mood, or apathy.

DE-JANet: A unified network based on dual encoder and joint attention for Alzheimer’s disease classification using multi-modal data

Structural magnetic resonance imaging (sMRI), which can reflect cerebral atrophy, plays an important role in the early detection of Alzheimer’s disease (AD). However, the information provided by analyzing only the morphological changes in sMRI is relatively limited, and the assessment of the atrophy degree is subjective. Therefore, it is meaningful to combine sMRI with other clinical information to acquire complementary diagnosis information and achieve a more accurate classification of AD. Nevertheless, how to fuse these multi-modal data effectively is still challenging. In this paper, we propose DE-JANet, a unified AD classification network that integrates image data sMRI with non-image clinical data, such as age and Mini-Mental State Examination (MMSE) score, for more effective multi-modal analysis. DE-JANet consists of three key components: (1) a dual encoder module for extracting low-level features from the image and non-image data according to specific encoding regularity, (2) a joint attention module for fusing multi-modal features, and (3) a token classification module for performing AD-related classification according to the fused multi-modal features. Our DE-JANet is evaluated on the ADNI dataset, with a mean accuracy of 0.9722 and 0.9538 for AD classification and mild cognition impairment (MCI) classification, respectively, which is superior to existing methods and indicates advanced performance on AD-related diagnosis tasks.

Classification of Mild Cognitive Impairment with Deep Transfer Learning Approach using CWT based Scalogram Images

Mild Cognitive Impairment (MCI) is a condition that can occur as a person gets older, and faces problems like recognition, memory, and language skills. Early detection of MCI is crucial, as it can progress to more severe conditions like Alzheimer's disease. This study proposes a method to use Scalogram images, obtained by applying Continuous Wavelet Transform (CWT) to EEG signals and pre-trained models like ResNet50, VGG16, InceptionV3, Inception_ResNetV2 through transfer learning to classify MCI and Healthy Control (HC). Fine-tuning of the models is also used to improve the results, and various performance metrics are employed for classification. The study concludes that Inception_ResNetV2 transfer learning yielded good results, while ResNet50 and InceptionV3 transfer learning with fine-tuning resulted in higher accuracy using a low learning rate.