Plasma Alzheimer's biomarkers and brain amyloid in Hispanic and non-Hispanic older adults.

INTRODUCTION: Alzheimer's disease studies often lack ethnic diversity. METHODS: We evaluated associations between plasma biomarkers commonly studied in Alzheimer's (p-tau181, GFAP, and NfL), clinical diagnosis (clinically normal, amnestic MCI, amnestic dementia, or non-amnestic MCI/dementia), and Aß-PET in Hispanic and non-Hispanic older adults. Hispanics were predominantly of Cuban or South American ancestry. RESULTS: Three-hundred seventy nine participants underwent blood draw (71.9 ± 7.8 years old, 60.2% female, 57% Hispanic of which 88% were Cuban or South American) and 240 completed Aß-PET. P-tau181 was higher in amnestic MCI (p = 0.004, d = 0.53) and dementia (p < 0.001, d = 0.97) than in clinically normal participants and discriminated Aß-PET[+] and Aß-PET[-] (AUC = 0.86). P-tau181 outperformed GFAP and NfL. There were no significant interactions with ethnicity. Among amnestic MCI, Hispanics had lower odds of elevated p-tau181 than non-Hispanic (OR = 0.41, p = 0.006). DISCUSSION: Plasma p-tau181 informs etiological diagnosis of cognitively impaired Hispanic and non-Hispanic older adults. Hispanic ethnicity may relate to greater likelihood of non-Alzheimer's contributions to memory loss. HIGHLIGHTS: Alzheimer's biomarkers were measured in Hispanic and non-Hispanic older adults. Plasma p-tau181 related to amnestic cognitive decline and brain amyloid burden. AD biomarker associations did not differ between Hispanic and non-Hispanic ethnicity. Hispanic individuals may be more likely to have non-Alzheimer causes of memory loss.

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic-Angular Rate-Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion.

In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic-angular rate-gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference ("ground truth") MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human-computer interaction, kinesiology, motor rehabilitation, etc.

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algorithm for predicting the progression of Alzheimer's disease using a distributed multimodal, multitask learning method. More specifically, each individual task is defined as a regression model, which predicts cognitive scores at a single time point. Since the prediction tasks for multiple intervals are related to each other in chronological order, multitask regression models have been developed to track the relationship between subsequent tasks. Furthermore, since subjects have various combinations of recording modalities together with other genetic, neuropsychological and demographic risk factors, special attention is given to the fact that each modality may experience a specific sparsity pattern. The model is hence generalized by exploiting multiple individual multitask regression coefficient matrices for each modality. The outcome for each independent modality-specific learner is then integrated with complementary information, known as risk factor parameters, revealing the most prevalent trends of the multimodal data. This new feature space is then used as input to the gradient boosting kernel in search for a more accurate prediction. This proposed model not only captures the complex relationships between the different feature representations, but it also ignores any unrelated information which might skew the regression coefficients. Comparative assessments are made between the performance of the proposed method with several other well-established methods using different multimodal platforms. The results indicate that by capturing the interrelatedness between the different modalities and extracting only relevant information in the data, even in an incomplete longitudinal dataset, will yield minimized prediction errors.

90 Y SPECT/CT quantitative study and comparison of uptake with pretreatment 99 m Tc-MAA SPECT/CT in radiomicrosphere therapy.

INTRODUCTION: Yttrium-90 (90 Y) microsphere post-treatment imaging reflects the true distribution characteristics of microspheres in the tumor and liver compartments. However, due to its decay spectra profile lacking a pronounced photopeak, the bremsstrahlung imaging for 90 Y has inherent limitations. The absorbed dose calculations for 90 Y microspheres radiomicrosphere therapy (RMT) sustain a limitation due to the poor quality of 90 Y imaging. The aim of this study was to develop quantitative methods to improve the post-treatment 90 Y bremsstrahlung single photon emission tomography (SPECT)/computed tomography (CT) image analysis for dosimetric purposes and to perform a quantitative comparison with the 99m Tc-MAA SPECT/CT images, which is used for theranostics purposes for liver and tumor dosimetry. METHODS: Pre and post-treatment SPECT/CT data of patients who underwent RMT for primary or metastatic liver cancer were acquired. A Jasczak phantom with eight spherical inserts of various sizes was used to obtain optimal iteration number for the contrast recovery algorithm for improving 90 Y bremsstrahlung SPECT/CT images. Comparison of uptake on 99m Tc-MAA and 90 Y microsphere SPECT/CT images was assessed using tumor to healthy liver ratios (TLRs). The voxel dosimetry technique was used to estimate absorbed doses. Absorbed doses within the tumor and healthy part of the liver were also investigated for correlation with administered activity. RESULTS: Improvement in CNR and contrast recovery coefficients on patient and phantom 90 Y bremsstrahlung SPECT/CT images respectively were achieved. The 99m Tc-MAA and 90 Y microspheres SPECT/CT images showed significant uptake correlation (r = 0.9, P = 0.05) with mean TLR of 9.4 ± 9.2 and 5.0 ± 2.2, respectively. The correlation between the administered activity and tumor absorbed dose was weak (r = 0.5, P > 0.05), however, healthy liver absorbed dose increased with administered activity (r = 0.8, P = 0.0). CONCLUSIONS: This study demonstrated correlation in mean TLR between 99m Tc-MAA and 90 Y microsphere SPECT/CT.

A practical guideline for intracranial volume estimation in patients with Alzheimer's disease.

BACKGROUND: Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure. METHODS: Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups. RESULTS: Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study. CONCLUSIONS: This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks.

BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. RESULTS: The study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. CONCLUSIONS: The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.

Classification of fMRI patterns--a study of the language network segregation in pediatric localization related epilepsy.

This article describes a pattern classification algorithm for pediatric epilepsy using fMRI language-related activation maps. 122 fMRI datasets from a control group (64) and localization related epilepsy patients (58) provided by five children's hospitals were used. Each subject performed an auditory description decision task. Using the artificial data as training data, incremental Principal Component Analysis was used in order to generate the feature space while overcoming memory requirements of large datasets. The nearest-neighbor classifier (NNC) and the distance-based fuzzy classifier (DFC) were used to perform group separation into left dominant, right dominant, bilateral, and others. The results show no effect of age, age at seizure onset, seizure duration, or seizure etiology on group separation. Two sets of parameters were significant for group separation, the patient vs. control populations and handedness. Of the 122 real datasets, 90 subjects gave the same classification results across all the methods (three raters, LI, bootstrap LI, NNC, and DFC). For the remaining datasets, 18 cases for the IPCA-NNC and 21 cases for the IPCA-DFC agreed with the majority of the five classification results (three visual ratings and two LI results). Kappa values vary from 0.59 to 0.73 for NNC and 0.61 to 0.75 for DFC, which indicate good agreement between NNC or DFC with traditional methods. The proposed method as designed can serve as an alternative method to corroborate existing LI and visual rating classification methods and to resolve some of the cases near the boundaries in between categories.

The effects of pediatric epilepsy on a language connectome.

This study introduces a new approach for assessing the effects of pediatric epilepsy on a language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI. An auditory word definition decision task paradigm was used to activate the language network for 29 patients and 30 controls. Evaluations illustrated that pediatric epilepsy is associated with a network efficiency reduction. Patients showed a propensity to inefficiently use the whole brain network to perform the language task; whereas, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was performed. The analysis revealed substantial global network feature differences between the patients and controls for the extent of activation network. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency toward randomness. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. We finally showed that a clustering scheme was able to fairly separate the subjects into their respective patient or control groups. The clustering was initiated using local and global nodal measurements. Compared to the intensity of activation network, the extent of activation network clustering demonstrated better precision. This ascertained that the network differences presented by the networks were associated with pediatric epilepsy.

Significance of normalization on anatomical MRI measures in predicting Alzheimer's disease.

This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer's disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or mean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD.

Quasi-stationarity of EEG for intraoperative monitoring during spinal surgeries.

We present a study and application of quasi-stationarity of electroencephalogram for intraoperative neurophysiological monitoring (IONM) and an application of Chebyshev time windowing for preconditioning SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. This method is shown empirically to be more clinically viable than present day approaches. In all twelve cases, the algorithm takes 4 sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

Induced effects of transcranial magnetic stimulation on the autonomic nervous system and the cardiac rhythm.

Several standard protocols based on repetitive transcranial magnetic stimulation (rTMS) have been employed for treatment of a variety of neurological disorders. Despite their advantages in patients that are retractable to medication, there is a lack of knowledge about the effects of rTMS on the autonomic nervous system that controls the cardiovascular system. Current understanding suggests that the shape of the so-called QRS complex together with the size of the different segments and intervals between the PQRST deflections of the heart could predict the nature of the different arrhythmias and ailments affecting the heart. This preliminary study involving 10 normal subjects from 20 to 30 years of age demonstrated that rTMS can induce changes in the heart rhythm. The autonomic activity that controls the cardiac rhythm was indeed altered by an rTMS session targeting the motor cortex using intensity below the subject's motor threshold and lasting no more than 5 minutes. The rTMS activation resulted in a reduction of the RR intervals (cardioacceleration) in most cases. Most of these cases also showed significant changes in the Poincare plot descriptor SD2 (long-term variability), the area under the low frequency (LF) power spectrum density curve, and the low frequency to high frequency (LF/HF) ratio. The RR intervals changed significantly in specific instants of time during rTMS activation showing either heart rate acceleration or heart rate deceleration.

Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human-Computer Interaction.

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human-computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDµK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human-computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials.

Free-water imaging reveals unique brain microstructural deficits in hispanic individuals with Dementia.

Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer's disease. For this cross-sectional study, we examined 108 participants (61 Hispanic vs. 47 Non-Hispanic individuals) from the 1Florida Alzheimer's Disease Research Center (1Florida ADRC), who were evaluated at baseline with diffusion-weighted and T1-weighted imaging, and positron emission tomography (PET) amyloid imaging. We used FreeSurfer to segment 34 cortical regions of interest. Baseline Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used as measures of cognitive performance. Group analyses assessed free-water measures (FW) and volume. Statistically significant FW regions based on ethnicity x group interactions were used in a stepwise regression function to predict total MMSE and MoCA scores. Random forest models were used to identify the most predictive brain-based measures of a dementia diagnosis separately for Hispanic and non-Hispanic groups. Results indicated elevated FW values for the left inferior temporal gyrus, left middle temporal gyrus, left banks of the superior temporal sulcus, left supramarginal gyrus, right amygdala, and right entorhinal cortex in Hispanic AD subjects compared to non-Hispanic AD subjects. These alterations occurred in the absence of different volumes of these regions in the two AD groups. FW may be useful in detecting individual differences potentially reflective of varying etiology that can influence cognitive decline and identify MRI predictors of cognitive performance, particularly among Hispanics.

Different aspects of failing to recover from proactive semantic interference predicts rate of progression from amnestic mild cognitive impairment to dementia.

Introduction: This study investigated the role of proactive semantic interference (frPSI) in predicting the progression of amnestic Mild Cognitive Impairment (aMCI) to dementia, taking into account various cognitive and biological factors. Methods: The research involved 89 older adults with aMCI who underwent baseline assessments, including amyloid PET and MRI scans, and were followed longitudinally over a period ranging from 12 to 55 months (average 26.05 months). Results: The findings revealed that more than 30% of the participants diagnosed with aMCI progressed to dementia during the observation period. Using Cox Proportional Hazards modeling and adjusting for demographic factors, global cognitive function, hippocampal volume, and amyloid positivity, two distinct aspects of frPSI were identified as significant predictors of a faster decline to dementia. These aspects were fewer correct responses on a frPSI trial and a higher number of semantic intrusion errors on the same trial, with 29.5% and 31.6 % increases in the likelihood of more rapid progression to dementia, respectively. Discussion: These findings after adjustment for demographic and biological markers of Alzheimer's Disease, suggest that assessing frPSI may offer valuable insights into the risk of dementia progression in individuals with aMCI.

Age association of language task induced deactivation induced in a pediatric population.

Task-induced deactivation (TID) potentially reflects the interactions between the default mode and task specific networks, which are assumed to be age dependent. The study of the age association of such interactions provides insight about the maturation of neural networks, and lays out the groundwork for evaluating abnormal development of neural networks in neurological disorders. The current study analyzed the deactivations induced by language tasks in 45 right-handed normal controls aging from 6 to 22 years of age. Converging results from GLM, dual regression and ROI analyses showed a gradual reduction in both the spatial extent and the strength of the TID in the DMN cortices as the brain matured from kindergarten to early adulthood in the absence of any significant change in task performance. The results may be ascribed to maturation leading to either improved multi-tasking (i.e. reduced deactivation) or reduced cognitive demands due to greater experience (affects both control and active tasks but leads to reduced overall difference). However, other effects, such as changes in the DMN connectivity that were not included in this study may also have influenced the results. In light of this, researchers should be cautious when investigating the maturation of DMN using TID. With a GLM analysis using the concatenated fMRI data from several paradigms, this study additionally identified an age associated increase of TID in the STG (bilateral), possibly reflecting the role of this area in speech perception and phonological processing.

A decisional space for fMRI pattern separation using the principal component analysis--a comparative study of language networks in pediatric epilepsy.

Atypical functional magnetic resonance imaging (fMRI) language patterns may be identified by visual inspection or by region of interest (ROI)-based laterality indices (LI) but are constrained by a priori assumptions. We compared a data-driven novel application of principal component analysis (PCA) to conventional methods. We studied 122 fMRI data sets from control and localization-related epilepsy patients provided by five children's hospitals. Each subject performed an auditory description decision task. The data sets, acquired with different scanners but similar acquisition parameters, were processed through fMRIB software library to obtain 3D activation maps in standard space. A PCA analysis was applied to generate the decisional space and the data cluster into three distinct activation patterns. The classified activation maps were interpreted by (1) blinded reader rating based on predefined language patterns and (2) by language area ROI-based LI (i.e., fixed threshold vs. bootstrap approaches). The different classification results were compared through κ inter-rater agreement statistics. The unique decisional space classified activation maps into three clusters (a) lower intensity typical language representation, (b) higher intensity typical, as well as (c) higher intensity atypical representation. Inter-rater agreements among the three raters were excellent (Fleiss κ = 0.85, P = 0.05). There was substantial to excellent agreement between the conventional visual rating and LI methods (κ = 0.69-0.82, P = 0.05). The PCA-based method yielded excellent agreement with conventional methods (κ = 0.82, P = 0.05). The automated and data-driven PCA decisional space segregates language-related activation patterns in excellent agreement with current clinical rating and ROI-based methods.

A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.

PURPOSE: Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mechanism driven by an integrated ML model to predict disease trajectory in a 2-year longitudinal study. The main aim of this study is to help capture visually in 2D and 3D renderings the diagnosis and prognosis of AD, therefore augmenting our understanding of the processes of multiclass classification and regression analysis. METHOD: The proposed method, Machine Learning for Visualizing AD (ML4VisAD), is designed to predict disease progression through a visual output. This newly developed model takes baseline measurements as input to generate a color-coded visual image that reflects disease progression at different time points. The architecture of the network relies on convolutional neural networks. With 1123 subjects selected from the ADNI QT-PAD dataset, we use a 10-fold cross-validation process to evaluate the method. Multimodal inputs* include neuroimaging data (MRI, PET), neuropsychological test scores (excluding MMSE, CDR-SB, and ADAS to avoid bias), cerebrospinal fluid (CSF) biomarkers with measures of amyloid beta (ABETA), phosphorylated tau protein (PTAU), total tau protein (TAU), and risk factors that include age, gender, years of education, and ApoE4 gene. FINDINGS/RESULTS: Based on subjective scores reached by three raters, the results showed an accuracy of 0.82 ± 0.03 for a 3-way classification and 0.68 ± 0.05 for a 5-way classification. The visual renderings were generated in 0.08 msec for a 23 × 23 output image and in 0.17 ms for a 45 × 45 output image. Through visualization, this study (1) demonstrates that the ML visual output augments the prospects for a more accurate diagnosis and (2) highlights why multiclass classification and regression analysis are incredibly challenging. An online survey was conducted to gauge this visualization platform's merits and obtain valuable feedback from users. All implementation codes are shared online on GitHub. CONCLUSION: This approach makes it possible to visualize the many nuances that lead to a specific classification or prediction in the disease trajectory, all in context to multimodal measurements taken at baseline. This ML model can serve as a multiclass classification and prediction model while reinforcing the diagnosis and prognosis capabilities by including a visualization platform.

Scenic routing navigation using property valuation.

Extensive prior work has provided methods for the optimization of routing based on weights assigned to travel duration, and/or travel cost, and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph in order to find the best route. Some users desire that the routing suggestion include consideration pertaining to the scenic-architectural quality of the path. For example, a user may seek a leisure walk via what they might deem as visually attractive architecture. Here, we are proposing a method to quantify such user preferences and scenic quality and to augment the standard routing methods by giving weight to the scenic quality. That is, instead of suggesting merely the time and cost-optimal route, we will find the best route that is tailored towards the user's scenic quality preferences as an additional criterion to the time and cost. The proposed method uniquely weighs the scenic interest or residential street segments based on the property valuation data.

Graph neural networks in EEG spike detection.

OBJECTIVE: This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. METHODS: Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. RESULTS: On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. CONCLUSION: Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. SIGNIFICANCE: This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.

Association of longitudinal cognitive decline with diffusion MRI in Gray Matter, Amyloid, and Tau deposition.

Extracellular amyloid plaques in gray matter are the earliest pathological marker for Alzheimer's disease (AD), followed by abnormal tau protein accumulation. The link between diffusion changes in gray matter, amyloid and tau pathology, and cognitive decline is not well understood. We first performed cross-sectional analyses on T1-weighted imaging, diffusion MRI, and amyloid and tau PETs from the ADNI 2/3 database. We evaluated cortical volume, free-water, fractional anisotropy (FA), and amyloid and tau SUVRs in 171 cognitively normal, 103 MCI, and 44 AD individuals. When the 3 groups were combined, increasing amyloid burden was associated with reduced extracellular free-water in the entorhinal cortex and hippocampus in those with amyloid-negative status whereas increasing tau burden was associated with increased extracellular free-water regardless of amyloid status. Next, we found that for the MCI subjects, diffusion measures (free-water, FA) alone predicted MMSE score 2 years later with a high r-square value (87%), as compared to tau SUVRs (27%), T1 volume (36%), and amyloid SUVRs (75%). Diffusion measures represent a potent non-invasive marker for predicting cognitive decline.