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This research adopts a big data approach to analyze students' academic performance in the digital era. Using secondary data from eighth-grade students and utilizing the SPSS statistical software, this research successfully identified significant factors influencing student performance. Discipline attitude, learning methods, and the habit of taking personal notes were found to have a high predictor importance in academic achievement. Correlation analysis among subjects revealed both positive and negative relationships, providing a deep understanding of students' skill interconnections. The success of this research cannot be separated from the important role of big data in providing a holistic overview of student performance, opening opportunities for more targeted recommendations. The findings of this research can serve as a foundation for the development of intervention strategies and more adaptive learning approaches, as well as making a significant contribution to directing education towards sustainability and improving student success in the future. With a better understanding of student performance patterns, this research highlights the revolutionary potential of big data in shaping the future of education. Abstrak Penelitian ini mengadopsi pendekatan big data untuk menganalisis kinerja akademik siswa dalam era digital. Dengan menggunakan data sekunder dari siswa kelas VIII dan memanfaatkan perangkat lunak statistik SPSS, penelitian ini berhasil mengidentifikasi faktor-faktor yang signifikan memengaruhi kinerja siswa. Sikap disiplin, metode belajar, dan kebiasaan membuat catatan pribadi terbukti memiliki predictor importance yang tinggi terhadap pencapaian akademik. Analisis korelasi antar mata pelajaran mengungkapkan pola hubungan positif dan negatif, memberikan pemahaman mendalam tentang interkoneksi keterampilan siswa. Keberhasilan penelitian ini tidak terlepas dari peran penting big data dalam memberikan gambaran holistik terhadap kinerja siswa, membuka peluang untuk rekomendasi yang lebih tepat sasaran. Hasil penelitian ini dapat menjadi landasan bagi pengembangan strategi intervensi dan pendekatan pembelajaran yang lebih adaptif, serta memberikan kontribusi penting dalam mengarahkan pendekatan pendidikan menuju keberlanjutan dan peningkatan kesuksesan siswa di masa depan. Dengan pemahaman yang lebih baik tentang pola kinerja siswa, penelitian ini menunjukkan potensi revolusioner big data dalam membentuk masa depan pendidikan.

Akira Toga,1,* Ayush Balaji,2,* Shigeki Nagura1 1Department of Orthopedic Surgery, Saiseikai Yokohama-shi Tobu Hospital, Yokohama, Kanagawa, Japan; 2Hull York Medical School, University of York, York, UK*These authors contributed equally to this workCorrespondence: Akira Toga, Department of Orthopedic Surgery, Saiseikai Yokohamashi Tobu Hospital, 3-6-1 Shimosueyoshi, Tsurumi-ku, Yokohama, Kanagawa, 230-8765, Japan, Tel +81-45-576-3000, Fax +81-45-576-3525, Email akira.toga@gmail.comBackground: Peripheral human bite wounds are rarely serious and are typically treated medically, with the most complex cases requiring only minor amputation or excision of the infected site. There are few to no reports documenting the development of purulent tenosynovitis, necrotizing fasciitis, and osteomyelitis from these lesions. Major amputations are required only rarely in these cases.Case Presentation: A 71-year-old woman presented with an uncontrolled infection following a self-inflicted bite wound to her left middle finger. A bacterial culture of the lesion revealed methicillin-resistant Staphylococcus aureus (MRSA). The infection could not be controlled with antibiotics or additional interventions, including debridement and minor amputation. She contracted severe COVID-19 while in the hospital which limited the available treatment options. In an attempt to control the infection, the patient ultimately underwent a major amputation of the distal left forearm. While recovering from the procedure, the patient succumbed to septic shock and cardiopulmonary arrest.Conclusion: The unusual progression of this case may be attributed to the interventions required to treat acute COVID-19 as well as a variety of confounding factors. For example, vasopressors and steroids used to treat severely-ill patients compromise the local and systemic physiologic responses to acute bacterial infection. It is important to reconsider clinical expectations during the pandemic and intervene as early as possible to prevent ongoing damage and clinical deterioration.Keywords: COVID-19, amputation, bite wound, necrotizing fasciitis, gangrene, purulent tenosynovitis, immunocompromised

The significant increase in COVID-19 cases in Indonesia in May-July 2021 overwhelmed health workers. One of the efforts to monitor the spread of COViD-19 disease is collecting data on patients and proper monitoring. For example, the Sukorame Community Health Center, Mojoroto Kediri, does not yet have an application to record and monitor COVID-19 patients. Data collection is currently done manually by writing in books and excel. This study designed and built a data collection and monitoring application for COVID-19 patients to help Puskesmas staff obtain more accurate patient data and monitor the related patient data. This study implements the waterfall method, including system requirements, design, implementation, verification, and maintenance. The results of this study are the applications that can help and facilitate Community Health Center in collecting data on COVID-19 as a form of effort in overcoming and preventing the spread of COVID-19 in the work area of Sukorame Community Health Center, Kediri City. Based on the user satisfaction questionnaire results, 75% of users consisting of staff and heads of community health centers were helped by this application.

The Department of Environment, Hygiene, and Parks is an agency that takes care of cleanliness and landscaping in Kediri. The distribution of parks in the Kediri City area is data and managed using a manual system using Microsoft Excel to get results in reports. For this reason, the Department of Environment, Cleanliness, and Parks requires an application that can be used to help manage data on the distribution of parks in the Kediri City area. The required system can manage park distribution data, display a park distribution map, and display the scale of conditions. The Geographic Information System for the Distribution of Parks in the City of Kediri was designed and built to meet the needs of the relevant agencies. Users of this system consist of visitors, admins, and heads of services. The system is designed and implemented using PHP, HTML, CSS, JavaScript, Mysql, and Laravel Framework, then integration with Google Maps API. The final result of this research is a Geographic Information System for the distribution of web-based parks in the City of Kediri using the Google map API as a provider of free maps that are integrated into the website. In addition, of course, it provides information about the distribution of the park and the scale of conditions.

Ayush Balaji, Akira Toga, Jun Kano, Atsuki Fujimaru, Taisuke Matsumoto, Shojiro Katoh Department of Orthopedic Surgery, Edogawa Hospital, Tokyo, JapanCorrespondence: Akira TogaDepartment of Orthopedic Surgery, Edogawa Hospital, 2-24-18 Higashikoiwa, Edogawa-ku, Tokyo, 160-8582, JapanTel +81-3-3673-1221Fax +81-3-3673-1229Email akira.toga@gmail.comAbstract: We describe the case of a 76-year-old Asian female patient who presented with severe pain and a valgus deformity of the right knee. Her past medical history included neurofibromatosis, which resulted in a severe anterior slope of the right knee, limb shortening, and congenital pseudarthrosis. She was diagnosed with severe anterolateral osteoarthritis and eburnation of the right knee that was treated surgically with a lateral unicompartmental knee arthroplasty (UKA). Bone and cartilage fragments were removed from the joint space and a UKA implant (Zimmer®) with dimensions of 29 mm × 50 mm was inserted. Perioperative imaging revealed that the procedure resulted in the correction of the valgus deformity. Pain was reduced and the patient was able to walk for 10 meters with support during the immediate postoperative period. One week post-surgery, the patient sustained an oblique tibial fracture that extended from the medial edge of the implant to the medial slope of the proximal tibia. This complication may have been attributed to large implant size or sagittal overcutting. The fracture was treated surgically with a rotated anterolateral locking plate (A.L.P.S®) inserted into the distal tibia. The patient was capable of ambulation at full weight load at two months after the second procedure. It is critical to recognize that there are no standard protocols that can be used to guide the treatment of neurofibromatosis-induced osteoarthritis. The specific preoperative condition of the individual patient plays a large role in determining the appropriate treatment option. In this case, the availability of custom-fitted UKA implants might have improved outlook, we understand that these devices are costly and may not be available at all hospitals. However, we strongly believe that the “gold standard” in these cases is patient-specific treatment that addresses the issues of the highest concern using the resources that are available at that time.Keywords: skeletal deformities, limb shortening, stress fracture, knee implants

Tuberculosis (TB) is a chronic disease that is still a public health problem globally, including Indonesia, due to its easy transmission. Treatment for TB sufferers consist of several drug combinations that are intended to eradicate germs. For TB sufferers, the key to successful treatment is the patient's compliance with taking medication every day. The lengthy treatment time is usually at least six months allowing non-adherence to taking the medication by the patient. If not treated properly, there will be a risk of disease complications, such as tuberculosis bacteria resistant to drugs, making TB treatment more difficult. In this study, an Android-based was built to remind TB sufferers to take their medication during the treatment process. In addition to the mobile-based application, there is also a web application used by drug drinking supervisors (PMO; Petugas Minum Obat in Indonesia) in monitoring TB patients, where the application can also view patient compliance statistics in taking medication and historical data on TB patients’ medical treatment. After completion and development, the application will be given to PMO officers and patients to be tested. It is hoped that the application can help the TB treatment process become more effective and prevent treatment failure from the implementation.

The process of representing the distribution of larva free numbers in the district of Kediri which is not visible makes the monitoring process for larva free rates per region less effective and efficient. Therefore, a geographic information system is needed to describe the distribution of larva free numbers in the Kediri Regency area. The design and development of this information system is carried out using two methods, one of which is the data collection method by conducting interviews with one of the staff in the Kediri district Health Office, observing and looking for references related to research.The existence of a Geographical Information System for the Distribution of Larva Free Numbers, helps the Kediri District Health Office in terms of recapitulating data and monitoring the risk of the distribution of larva numbers in the Kediri Regency area. The Geographical Information System for the Distribution of Larva Free Numbers in Kediri Regency was successfully designed and built and can display the distribution of larva free numbers in the form of markers on a map. The Geographical Information System for the Distribution of Larva Free Numbers in Kediri Regency uses PHP, HTML, CSS, JavaScript, MySQL, and the laravel framework.

Akira Toga,1 Ayush Balaji,2 Taisuke Matsumoto,1 Atsuki Fujimaru,1 Hideaki Murakami,1 Shojiro Katoh1 1Department of Orthopedic Surgery, Edogawa Hospital, Tokyo, Japan; 2Yokohama International School, Yokohama, JapanCorrespondence: Akira TogaDepartment of Orthopedic Surgery, Edogawa Hospital, 2-24-18 Higashikoiwa, Edogawa-ku, Tokyo 160-8582, JapanTel +81-3-3673-1221Fax +81-3-3673-1229Email akira.toga@gmail.comAbstract: The present case report describes a pediatric patient who presented with flexor digitorum profundus (FDP) entrapment after a forearm fracture. The patient was diagnosed with a Bado type I Monteggia fracture. The fracture was reduced using closed reduction under fluoroscopy followed by Kirschner’s wire fixation. This case is unique because the FDP was found entrapped in the fracture site 2 weeks post-operation and was managed using conservative therapy. FDP entrapment is typically managed by surgical intervention, and there have been no previous reports of conservative management. The FDP was released using passive extension of the index finger under general anesthesia, and no irreversible damage to the tendon or muscle was found. This case report demonstrates the potential for conservative therapy in the management of FDP entrapment after forearm fractures.Keywords: pediatric fracture, entrapment of FDP, conservative therapy, dynamic tenodesis effect, forearm fracture, rehabilitation

Dalam implementasi big data biasanya membutuhkan sumber daya yang cukup besar untuk dapat melakukan analisis terhadap data-data yang jumlahnya sangat besar tersebut, hal ini biasanya menjadi kendala dikarenakan keterbatasan sumber daya yang dimiliki. Komputasi awan (cloud computing) yang salah satunya mempunyai sifat elasticity di dalamnya, menawarkan solusi keterbatasan sumber daya ini. Sumber daya yang terbatas misalkan dalam hal processor, RAM atau storage, dapat digabungkan dengan sumber daya yang dimiliki public cloud provider yang tersedia di market. Sehingga penggabungan 2 sumber daya ini, private cloud dan public cloud, diharapkan menjadi solusi untuk dapat mengimplementasikan analisis big data yang dapat diterapkan untuk analisis berbagai macam bidang. Secara umum penelitian ini bertujuan untuk mengimplementasikan hybrid cloud yang menggabungkan sumber daya dari private cloud dengan sumber daya dari public cloud sebagai infrastruktur untuk analisis big data. Secara khusus tujuan penelitian ini adalah merumuskan sebuah metode minimalisasi cost dalam pemilihan public cloud dengan pendekatan sistem pendukung keputusan menggunakan Fuzzy AHP pada pemilihan public cloud. Langkah pertama yang dilakukan dalam penelitian ini adalah pengumpulan data cost penggunaan resource dari public cloud. Selanjutnya dilakukan analisis kebutuhan sumber daya yang diperlukan untuk melalukan analisis big data dengan studi kasus topik tertentu. Selanjutnya tahap analisis terhadap pemilihan public cloud yang tepat untuk digunakan sumber dayanya dengan pertimbangan minimalisasi cost. Langkah terakhir adalah implementasi hybrid cloud dan melakukan analisis dan evaluasi terhadap metode yang diusulkan.

IntroductionAlthough dementia-related proteinopathy has a strong negative impact on public health, and is highly heritable, understanding of the related genetic architecture is incomplete.MethodsWe applied multidimensional generalized partial credit modeling (GPCM) to test genetic associations with dementia-related proteinopathies. Data were analyzed to identify candidate single nucleotide variants for the following proteinopathies: Aβ, tau, α-synuclein, and TDP-43.ResultsFinal included data comprised 966 participants with neuropathologic and WGS data. Three continuous latent outcomes were constructed, corresponding to TDP-43-, Aβ/Tau-, and α-synuclein-related neuropathology endophenotype scores. This approach helped validate known genotype/phenotype associations: for example, TMEM106B and GRN were risk alleles for TDP-43 pathology; and GBA for α-synuclein/Lewy bodies. Novel suggestive proteinopathy-linked alleles were also discovered, including several (SDHAF1, TMEM68, and ARHGEF28) with colocalization analyses and/or high degrees of biologic credibility.DiscussionA novel methodology using GPCM enabled insights into gene candidates for driving misfolded proteinopathies.HighlightsLatent factor scores for proteinopathies were estimated using a generalized partial credit model. The three latent continuous scores corresponded well with proteinopathy severity. Novel genes associated with proteinopathies were identified. Several genes had high degrees of biologic credibility for dementia risk factors.

BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.FindingsThe application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).InterpretationOur results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.FundingThe specific funding of this article is provided in the acknowledgements section.

ObjectiveMicrotubule-associated protein tau (MAPT) mutations cause frontotemporal lobar degeneration, and novel biomarkers are urgently needed for early disease detection. We used task-free functional magnetic resonance imaging (fMRI) mapping, a promising biomarker, to analyze network connectivity in symptomatic and presymptomatic MAPT mutation carriers.MethodsWe compared cross-sectional fMRI data between 17 symptomatic and 39 presymptomatic carriers and 81 controls with (1) seed-based analyses to examine connectivity within networks associated with the 4 most common MAPT-associated clinical syndromes (ie, salience, corticobasal syndrome, progressive supranuclear palsy syndrome, and default mode networks) and (2) whole-brain connectivity analyses. We applied K-means clustering to explore connectivity heterogeneity in presymptomatic carriers at baseline. Neuropsychological measures, plasma neurofilament light chain, and gray matter volume were compared at baseline and longitudinally between the presymptomatic subgroups defined by their baseline whole-brain connectivity profiles.ResultsSymptomatic and presymptomatic carriers had connectivity disruptions within MAPT-syndromic networks. Compared to controls, presymptomatic carriers showed regions of connectivity alterations with age. Two presymptomatic subgroups were identified by clustering analysis, exhibiting predominantly either whole-brain hypoconnectivity or hyperconnectivity at baseline. At baseline, these two presymptomatic subgroups did not differ in neuropsychological measures, although the hypoconnectivity subgroup had greater plasma neurofilament light chain levels than controls. Longitudinally, both subgroups showed visual memory decline (vs controls), yet the subgroup with baseline hypoconnectivity also had worsening verbal memory and neuropsychiatric symptoms, and extensive bilateral mesial temporal gray matter decline.InterpretationNetwork connectivity alterations arise as early as the presymptomatic phase. Future studies will determine whether presymptomatic carriers' baseline connectivity profiles predict symptomatic conversion. ANN NEUROL 2023;94:632-646.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

BACKGROUND: Traumatic brain injury (TBI) can alter brain structure and lead to onset of persistent neuropsychological symptoms. This study investigates the relationship between brain injury and psychological distress after mild TBI using multimodal magnetic resonance imaging. METHODS: A total of 89 patients with mild TBI from the TRACK-TBI (Transforming Research and Clinical Knowledge in Traumatic Brain Injury) pilot study were included. Subscales of the Brief Symptoms Inventory 18 for depression, anxiety, and somatization were used as outcome measures of psychological distress approximately 6 months after the traumatic event. Glasgow Coma Scale scores were used to evaluate recovery. Magnetic resonance imaging data were acquired within 2 weeks after injury. Perivascular spaces (PVSs) were segmented using an enhanced PVS segmentation method, and the volume fraction was calculated for the whole brain and white matter regions. Cortical thickness and gray matter structures volumes were calculated in FreeSurfer; diffusion imaging indices and multifiber tracts were extracted using the Quantitative Imaging Toolkit. The analysis was performed considering age, sex, intracranial volume, educational attainment, and improvement level upon discharge as covariates. RESULTS: PVS fractions in the posterior cingulate, fusiform, and postcentral areas were found to be associated with somatization symptoms. Depression, anxiety, and somatization symptoms were associated with the cortical thickness of the frontal-opercularis and occipital pole, putamen and amygdala volumes, and corticospinal tract and superior thalamic radiation. Analyses were also performed on the two hemispheres separately to explore lateralization. CONCLUSIONS: This study shows how PVS, cortical, and microstructural changes can predict the onset of depression, anxiety, and somatization symptoms in patients with mild TBI.

IntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.MethodsADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.Results and discussionADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

Accurate measurement of Alzheimer's disease (AD) pathology in older adults without significant clinical impairment is critical to assessing intervention strategies aimed at slowing AD-related cognitive decline. The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (POINTER) is a 2-year randomized controlled trial to evaluate the effect of multicomponent risk reduction strategies in older adults (60-79 years) who are cognitively unimpaired but at increased risk for cognitive decline/dementia due to factors such as cardiovascular disease and family history. The POINTER Imaging ancillary study is collecting tau-PET ([18F]MK6240), beta-amyloid (Aβ)-PET ([18F]florbetaben [FBB]) and MRI data to evaluate neuroimaging biomarkers of AD and cerebrovascular pathophysiology in this at-risk sample. Here 481 participants (70.0±5.0; 66% F) with baseline MK6240, FBB and structural MRI scans were included. PET scans were coregistered to the structural MRI which was used to create FreeSurfer-defined reference regions and target regions of interest (ROIs). We also created off-target signal (OTS) ROIs to examine the magnitude and distribution of MK6240 OTS across the brain as well as relationships between OTS and age, sex, and race. OTS was unimodally distributed, highly correlated across OTS ROIs and related to younger age and sex but not race. Aiming to identify an optimal processing approach for MK6240 that would reduce the influence of OTS, we compared our previously validated MRI-guided standard PET processing and 6 alternative approaches. The alternate approaches included combinations of reference region erosion and meningeal OTS masking before spatial smoothing as well as partial volume correction. To compare processing approaches we examined relationships between target ROIs (entorhinal cortex (ERC), hippocampus or a temporal meta-ROI (MetaROI)) SUVR and age, sex, race, Aβ and a general cognitive status measure, the Modified Telephone Interview for Cognitive Status (TICSm). Overall, the processing approaches performed similarly, and none showed a meaningful improvement over standard processing. Across processing approaches we observed previously reported relationships with MK6240 target ROIs including positive associations with age, an Aβ+> Aβ- effect and negative associations with cognition. In sum, we demonstrated that different methods for minimizing effects of OTS, which is highly correlated across the brain within subject, produced no substantive change in our performance metrics. This is likely because OTS contaminates both reference and target regions and this contamination largely cancels out in SUVR data. Caution should be used when efforts to reduce OTS focus on target or reference regions in isolation as this may exacerbate OTS contamination in SUVR data.

Apathy is one of the most common neuropsychiatric symptoms (NPS) and is associated with poor clinical outcomes. Research that helps define the apathy phenotype is urgently needed, particularly for clinical and biomarker studies. We used latent class analysis (LCA) with two independent cohorts to understand how apathy and depression symptoms co-occur statistically. We further explored the relationship between latent class membership, demographics, and the presence of other NPS. The LCA identified a four-class solution (no symptoms, apathy, depression, and combined apathy/depression), reproducible over both cohorts, providing robust support for an apathy syndrome distinct from depression and confirming that an apathy/depression syndrome exists, supported by the model fit test with the four-class solution scores evidencing better fitting (Bayesian information criterion adjusted and entropy R 2). Using a data-driven method, we show distinct and statistically meaningful co-occurrence of apathy and depressive symptoms. There was evidence that these classes have different clinical associations, which may help inform diagnostic categories for research studies and clinical practice.HighlightsWe found four classes: no symptoms, apathy, depression and apathy/depression.Apathy conferred a higher probability for agitation.Apathy diagnostic criteria should include accompanying neuropsychiatric symptoms.

The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.

The model-free algorithms of reinforcement learning (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This generalized reinforcement learning (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL models RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

PURPOSE: Brain metastases (BM) remain a significant cause of morbidity and mortality in breast cancer (BC) patients. Specific factors promoting the process of BM and predilection for selected neuro-anatomical regions remain unknown, yet may have major implications for prevention or treatment. Anatomical spatial distributions of BM from BC suggest a predominance of metastases in the hindbrain and cerebellum. Systematic approaches to quantifying BM location or location-based analyses based on molecular subtypes, however, remain largely unavailable. METHODS: We analyzed stereotactic Cartesian coordinates derived from 134 patients undergoing gamma- knife radiosurgery (GKRS) for treatment of 407 breast cancer BMs to quantitatively study BM spatial distribution along principal component axes and by intrinsic molecular subtype (ER, PR, Herceptin). We used kernel density estimators (KDE) to highlight clustering and distribution regions in the brain, and we used the metric of mutual information (MI) to tease out subtle differences in the BM distributions associated with different molecular subtypes of BC. BM location maps according to vascular and anatomical distributions using Cartesian coordinates to aid in systematic classification of tumor locations were additionally developed. RESULTS: We corroborated that BC BMs show a consistent propensity to arise posteriorly and caudally, and that Her2+ tumors are relatively more likely to arise medially rather than laterally. To compare the distributions among varying BC molecular subtypes, the mutual information metric reveal that the ER-PR-Her2+ and ER-PR-Her2- subtypes show the smallest amount of mutual information and are most molecularly distinct. The kernel density contour plots show a propensity for triple negative BC to arise in more superiorly or cranially situated BMs. CONCLUSIONS: We present a novel and shareable workflow for characterizing and comparing spatial distributions of BM which may aid in identifying therapeutic or diagnostic targets and interactions with the tumor microenvironment. Further characterization of these patterns with larger multi-institutional data-sets may have major impacts on treatment or management of cancer patients.