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IntroductionComprehensive treatment of Alzheimer’s disease and related dementias (ADRD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. The Coaching for Cognition in Alzheimer’s (COCOA) trial was a prospective randomized controlled trial (RCT) to test the hypothesis that a remotely coached multimodal lifestyle intervention would improve early-stage Alzheimer’s disease (AD). AD results from the interplay of multiple interacting dysfunctional biological systems. Specific causes of AD differ between individuals. Personalized, multimodal therapies are needed to best prevent and treat AD. COCOA collected psychometric, clinical, lifestyle, genomic, proteomic, metabolomic and microbiome data at multiple timepoints across two years for each participant. These data enable systems-biology analyses. We report analyses of the first COCOA data freeze. This analysis includes an evaluation of the effect of the intervention on outcome measures. It also includes systems analyses to identify molecular mediators that convey the effect of personalized multimodal lifestyle interventions on amelioration of cognitive trajectory.MethodsA total of 55 participants with early-stage AD from Southern California were randomized into two parallel arms. Arm 1 (control; N=24) received standard of care. Arm 2 (intervention; N=31) also received telephonic personalized coaching for multiple lifestyle interventions including diet, exercise, and cognitive training. COCOA’s overarching aim was to gather dense molecular data from an AD cohort to improve understanding of pathophysiology and advance treatment. For the RCT, COCOA’s objective was to test the hypothesis that the Memory Performance Index (MPI) trajectory would be better in the intervention arm than in the control arm. The Functional Assessment Staging Test (FAST) was assessed for a secondary outcome. Assessments were blinded. The nature of the intervention precluded participant blinding.ResultsThe intervention arm ameliorated 2.6 ± 0.8 MPI points (p = 0.0007; N = 48) compared to the control arm over the two-year intervention. Top-ranked candidate mediators included: albumin, propionylcarnitine, sphingomyelin, hexadecanedioate, acetylkynurenine, tiglylcarnitine, IL18R1, palmitoyl-sphingosine-phosphoethanolamine, acetyltryptophan, and IL17D. These individual molecules implicated inflammatory and nitrogen/tryptophan metabolism pathways. No important adverse events or side effects were observed.ConclusionsClinical trials should include frequent assessment of dense data to maximize knowledge gained. Such knowledge is useful not only in testing a primary hypothesis, but also in advancing basic biological and pathophysiological knowledge, understanding mechanisms explaining trial results, generating synergistic knowledge tangential to preconceived hypotheses, and refining interventions for clinical translation. Data from every trial should allow an intervention to be refined and then tested in future trials, driving iterative improvement. Multimodal lifestyle interventions are effective for ameliorating cognitive decline and may have an effect size larger than pharmacological interventions. Effects may be molecularly idiosyncratic; personalization of interventions is important. Dietary changes and exercise are likely to be beneficial components of multimodal interventions in many individuals. Remote coaching is an effective intervention for early stage ADRD. Remote interventions were effective during the COVID pandemic.

Background: Recent Alzheimer’s disease (AD) trials have faced significant challenges to enroll pre-symptomatic or early stage AD subjects with biomarker positivity, minimal or no cognitive impairment, and likelihood to decline cognitively during a short trial period. Our previous study showed that digital cognitive biomarkers (DCB), generated by a hierarchical Bayesian cognitive process (HBCP) model, were able to distinguish groups of cognitively normal individuals with impending cognitive decline from those without. We generated DCBs using only baseline Auditory Verbal Learning Test’s wordlist memory (WLM) item response data from the Mayo Clinic Alzheimer’s Disease Patient Registry. Objectives: To replicate our previous findings, using baseline ADAS-Cog WLM item response data from the Alzheimer’s Disease Neuroimaging Initiative, and compare DCBs to traditional approaches for scoring word-list memory tests. Design: Classified decliner subjects (n = 61) as those who developed amnestic MCI or AD dementia within 3 years of normal baseline assessment and non-decliner (n = 442) as those who did not. Measures: Evaluated the relative value of DCBs compared to traditional measures, using three analytic approaches to group differences: 1) logistic regression of summary scores per ADAS-Cog WLM task; 2) Bayesian modeling of summary scores; and 3) HBCP modeling to generate DCBs from item-level responses. Results: The HBCP model produced posterior distributions of group differences, of which Bayes factor assessment identified three DCBs with notable group differences: Immediate Retrieval from Durable Storage, (BFds = 11.8, strong evidence); One-Shot Learning, (BFds = 4.5, moderate evidence); and Partial Learning (BFds = 2.9, weak evidence). In contrast, logistic regression of summary scores did not significantly discriminate between groups, and the Bayes factor assessment of modeled summary scores provided moderate evidence that the groups were equivalent (BFsd = 3.4, 3.1, 2.9, and 1.4, respectively). Conclusions: This study demonstrated DCBs’ ability to distinguish , at baseline, between impending cognitive decline and non-decline groups where individuals in both groups were classified as cognitively normal. This validated findings from our previous study, demonstrating DCBs’ advantages over traditional approaches. This study warrants further refinement of the HBCP DCBs to predict impending cognitive decline in individuals and other factors associated with AD, such as physical biomarker load.

Comprehensive treatment of Alzheimer's disease (AD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. We present the design and methodology for the Coaching for Cognition in Alzheimer's (COCOA) trial. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. Monotherapies have had limited success. More interventional studies are needed to test the effectiveness of multimodal multi-domain therapies for dementia prevention and treatment. Multimodal therapies use multiple interventions to address multiple systemic causes and potentiators of cognitive decline and functional loss; they can be personalized, as different sets of etiologies and systems responsive to therapy may be present in different individuals. COCOA is designed to test the hypothesis that coached multimodal interventions beneficially alter the trajectory of cognitive decline for individuals on the spectrum of AD and related dementias (ADRD). COCOA is a two-arm prospective randomized controlled trial (RCT). COCOA collects psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints across 2 years for each participant. These data enable systems biology analyses. One arm receives standard of care and generic healthy aging recommendations. The other arm receives standard of care and personalized data-driven remote coaching. The primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. COCOA began enrollment in January 2018. We hypothesize that multimodal interventions will ameliorate cognitive decline and that data-driven health coaching will increase compliance, assist in personalizing multimodal interventions, and improve outcomes for patients, particularly for those in the early stages of the AD spectrum.The Coaching for Cognition in Alzheimer's (COCOA) trial tests personalized multimodal lifestyle interventions for Alzheimer's disease and related dementias.Dense longitudinal molecular data will be useful for future studies.Increased use of Hill's criteria in analyses may advance knowledge generation.Remote coaching may be an effective intervention.Because lifestyle interventions are inexpensive, they may be particularly valuable in reducing global socioeconomic disparities in dementia care.

Alzheimer disease (AD) is characterized by β-amyloid (Aβ) plaques and tau neurofibrillary tangles. There are several PET imaging biomarkers for Aβ including 11C-PiB and 18F-florbetapir. Recently, PET tracers for tau neurofibrillary tangles have become available and have shown utility in detection and monitoring of neurofibrillary pathology over time. Flortaucipir F 18 is one such tracer. Initial clinical studies indicated greater tau binding in AD and mild cognitive impairment patients than in controls in a pattern consistent with tau pathology observed at autopsy. However, little is known about the reproducibility of such findings. To our knowledge, this study reports the first data regarding test-retest reproducibility of flortaucipir F 18 PET. Methods: Twenty-one subjects who completed the study (5 healthy controls, 6 mild cognitive impairment, and 10 AD) received 370 MBq of flortaucipir F 18 and were imaged for 20 min beginning 80 min after injection and again at 110 min after injection. Follow-up (retest) imaging occurred between 48 h and 4 wk after initial imaging. Images were spatially normalized to Montreal Neurological Institute template space. SUVRs were calculated using AAL (Automated Anatomical Labeling atlas) volumes of interest (VOIs) for parietal, temporal, occipital, anterior, and posterior hippocampal, parahippocampal, and fusiform regions, as well as a posterior neocortical VOI composed of average values from parietal, temporal, and occipital areas. Further, a VOI derived by discriminant analysis that maximally separated diagnostic groups (multiblock barycentric discriminant analysis [MUBADA]) was used. All VOIs were referenced to a subsection of cerebellar gray matter (cere-crus) as well as a parametrically derived white matter-based reference region (parametric estimate of reference signal intensity [PERSI]). t test, correlation analyses, and intraclass correlation coefficient were used to explore test-retest performance. Results: Test-retest analyses demonstrated low variability in flortaucipir F 18 SUVR. The SD of mean percentage change between test and retest using the PERSI reference region was 2.22% for a large posterior neocortical VOI, 1.84% for MUBADA, 1.46% for frontal, 1.98% for temporal, 2.28% for parietal, and 3.27% for occipital VOIs. Further, significant correlations (R2 > 0.85; P < 0.001) were observed for all regions, and intraclass correlation coefficient values (test-retest consistency) were greater than 0.92 for all regions. Conclusion: Significant test-retest reproducibility for flortaucipir F 18 was found across neocortical and mesial temporal lobe structures. These preliminary data suggest that flortaucipir F 18 tau imaging could be used to examine changes in tau burden over time.

We examined whether using a medical food therapy for hyperhomocysteinemia (HHcy) in patients with Alzheimer's disease (AD) or cognitive impairment due to cerebrovascular disease (CVD) with Cerefolin®/CerefolinNAC® (CFLN: L-methylfolate, methylcobalamin, and N-acetyl-cysteine) slowed regional brain atrophy. Thirty HHcy patients with AD and related disorders (ADRD) received CFLN (HHcy+CFLN: duration [μ ± σ] = 18.6±16.1 months); a sub-sample of this group did not receive CFLN for varying periods of time (HHcy+NoCFLN: duration [μ ± σ] = 12.6±5.6 months). Thirty-seven NoHHcy patients with ADRD did not receive CFLN (NoHHcy+NoCFLN: duration [μ ± σ] = 13.3±17.7 months). No participant took supplemental B vitamins. Regional brain volumes were measured at baseline and end of study, and covariate-adjusted rates of hippocampal, cortical, and forebrain parenchymal (includes white matter) atrophy were predicted. The HHcy+CFLN group's hippocampal and cortical atrophy adjusted rates were 4.25 and 11.2 times slower than those of the NoHHcy+NoCFLN group (p

We report kinetic modeling results of dynamic acquisition data from 0 to 100 min after injection with the tau PET tracer 18F-AV-1451 in 19 subjects. Methods: Subjects were clinically diagnosed as 4 young cognitively normal, 5 old cognitively normal, 5 mild cognitive impairment, and 5 Alzheimer disease (AD). Kinetic modeling was performed using Logan graphical analysis with the cerebellum crus as a reference region. Voxelwise binding potential ( B P ) and SUV ratio ( S U V R 80 − 100 ) images were compared. Results: In AD subjects, slower and spatially nonuniform clearance from cortical regions was observed as compared with the controls, which led to focal uptake and elevated retention in the imaging data from 80 to 100 min after injection. BP from the dynamic data from 0 to 100 min correlated strongly (R2 > 0.86) with corresponding regional S U V R 80 − 100 − 1 values. In the putamen, the observed kinetics (positive S U V R 1 − 5 − 1 at the tracer delivery stage and plateauing time–SUVR curves for all diagnostic categories) may suggest either additional off-target binding or a second binding site with different kinetics. Conclusion: The kinetics of the 18F-AV-1451 tracer in cortical areas, as examined in this small group of subjects, differed by diagnostic stage. A delayed 80- to 100-min scan provided a reasonable substitute for a dynamic 0- to 100-min acquisition for cortical regions although other windows (e.g., 75–105 min) may be useful to evaluate.

We model word-list learning over sequences of immediate and delayed free recall tasks to study the impact of memory impairment on episodic memory. We use a previously developed Multinomial Processing Tree (MPT) model of encoding, retrieval, and learning (Alexander et al., 2016), and apply it to behavioral data from thousands of patients tested tens of thousands of times in a cognitive disorders clinic. The patients were independently diagnosed, using the Functional Assessment Staging Test (FAST), into six stages of impairment. We apply hierarchical and latent-mixture versions of the MPT model to patients in each FAST stage, exploring individual differences among people and item-position effects across the word lists. Our results show clear and theoretically interpretable regularities in how model parameters change over item positions, corresponding to standard primacy and recency effects in free recall. Accordingly, we develop an extended model that directly incorporates theoretical assumptions about serial position. Inferences from this model allow us to reach conclusions about how learning, encoding, and retrieval processes change as memory impairment progresses.

We study the effect of memory impairment on triadic comparisons of animal names in a large clinical data set. We define eight groups of subjects in terms of their delayed free recall performance, and present standard analyses of the triadic comparison and free recall data that provide little insight into the effect of memory impairment on semantic structure. We then develop and apply two new methods for analyzing the data, based on cognitive models and using Bayesian statistical inference. The first new method focuses on modeling changes in semantic representation, by inferring multidimensional scaling (MDS) representations for each group based on their triadic comparisons. These representations reveal a successive decrease in semantic cluster structure and increase in uncertainty with increasing impairment. We propose a measure of spatial organization as a means of quantifying the visually evident changes in semantic organization, and demonstrate its usefulness. The second new method focuses on modeling changes in memory access with impairment, inferring the extent to which each individual makes triadic comparisons consistent with a common semantic representation. Although these inferences are based on just 12 comparisons per subject, we show that they vary systematically with memory impairment group. We conclude by discussing the potential for clinical application of our new models, measures, and methods.

Aggregates of hyperphosphorylated tau (PHF-tau), such as neurofibrillary tangles, are linked to the degree of cognitive impairment in Alzheimer's disease. We have recently reported early clinical results of a novel PHF-tau targeting PET imaging agent, [F18]-T807. Since then, we have investigated a second novel PHF-tau targeting PET imaging agent, [F18]-T808, with different pharmacokinetic characteristics, which may be favorable for imaging Alzheimer's disease and other tauopathies. Here, we describe the first human brain images with [F18]-T808. © 2014 A IOS Press and the authors. All rights reserved.

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers.

Studies have produced conflicting results assessing hyperhomocysteinemia (HYH) treatment with B vitamins in patients with normal cognition, Alzheimer’s disease and related disorders (ADRD). This study examined the effect of HYH management with L-methylfolate (LMF), methylcobalamin (MeCbl; B12), and N-acetyl-cysteine (CFLN: Cerefolin®/Cerefolin-NAC®) on cognitive decline. Prospective, case-control study of subjects followed longitudinally. Outpatient clinic for cognitive disorders. 116 ADRD patients (34 with HYH, 82 with No-HYH) met inclusion and exclusion criteria to participate. No study participant took B vitamins. HYH patients received CFLN, and No-HYH patients did not. Cognitive outcome measures included MCI Screen (memory), CERAD Drawings (constructional praxis), Ishihara Number Naming (object recognition), Trails A and B (executive function), and F-A-S test (verbal fluency). Dependent or predictor measures included demographics, functional severity, CFLN and no CFLN treatment duration, ADRD diagnosis, memantine and cholinesterase inhibitor treatment. Linear mixed effects models with covariate adjustment were used to evaluate rate of change on cognitive outcomes. The duration of CFLN treatment, compared to an equivalent duration without CFLN treatment, significantly slowed decline in learning and memory, constructional praxis, and visual-spatial executive function (Trails B). CFLN treatment slowed cognitive decline significantly more for patients with milder baseline severity. CFLN treatment effect increased as baseline functional severity decreased. The analytical model showed that treatment duration must exceed some minimum period of at least one year to slow the rate of cognitive decline. After covariate adjustment, HYH+CFLN significantly slowed cognitive decline compared to No-HYH+No-CFLN. Longer CFLN treatment duration, milder baseline severity, and magnitude of homocysteine reduction from baseline were all significant predictors. There are a number of factors that could account for disagreement with other clinical trials of B vitamin treatment of HYH. Moreover, CFLN is chemically distinct from commonly used B vitamins as both LMF and MeCbl are the fully reduced and bioactive functional forms; CLFN also contains the glutathione precursor, N-acetyl-cysteine. The findings of other B vitamin trials of HYH can, therefore, only partly account for treatment effects of CFLN. These findings warrant further evaluation with a randomized, placebo-controlled trial.

a b s t r a c t The study of human episodic memory is a topic that interests cognitive and mathematical psychologists as well as clinicians interested in the diagnosis and assessment of Alzheimer's disease and related disorders (ADRD). In this paper, we use simple cognitive models for the recognition and recall tasks typically applied in clinical assessments of ADRD to study memory performance in ADRD patients. Our models make use of hierarchical Bayesian methods as a way to model individual differences in patient performance and to facilitate the modeling of performance changes that occur during multiple recall tasks. We show how the models are able to account for different aspects of patient performance, and also discuss some of the predictive capabilities of the model. We conclude with a discussion on the scope to improve on our results by discussing the link between memory theory in psychology and clinical practice.

Among the major impediments to the design of clinical trials for the prevention of Alzheimer's disease (AD), the most critical is the lack of validated biomarkers, assessment tools, and algorithms that would facilitate identification of asymptomatic individuals with elevated risk who might be recruited as study volunteers. Thus, the Leon Thal Symposium 2009 (LTS'09), on October 27–28, 2009 in Las Vegas, Nevada, was convened to explore strategies to surmount the barriers in designing a multisite, comparative study to evaluate and validate various approaches for detecting and selecting asymptomatic people at risk for cognitive disorders/dementia. The deliberations of LTS'09 included presentations and reviews of different approaches (algorithms, biomarkers, or measures) for identifying asymptomatic individuals at elevated risk for AD who would be candidates for longitudinal or prevention studies. The key nested recommendations of LTS'09 included: (1) establishment of a National Database for Longitudinal Studies as a shared research core resource; (2) launch of a large collaborative study that will compare multiple screening approaches and biomarkers to determine the best method for identifying asymptomatic people at risk for AD; (3) initiation of a Global Database that extends the concept of the National Database for Longitudinal Studies for longitudinal studies beyond the United States; and (4) development of an educational campaign that will address public misconceptions about AD and promote healthy brain aging.

Omentum transposition surgery (OT) applied to various neurodegenerative disorders has produced clinically significant improvement, which may be due to omentally-derived factors. To evaluate the clinical effect of left hemisphere OT in a primary progressive aphasia (PPA) patient, 3 year follow-up data were analysed.Left hemisphere OT was performed on a 68-year-old male with PPA, characterized by moderate dementia and severe expressive aphasia with relatively preserved comprehension, object recognition and visual-spatial abilities. He was longitudinally assessed with cognitive, functional, behavioral and brain HMPAO SPECT measures pre-OT, at baseline and every 3-6 months for 34 months.All measures improved above baseline for20 months and persisted at or above baseline for 34 months. Cortical activity increased by a maximum of 21% underneath transposed omentum and in synaptically connected areas, and persisted in50% of the cortex for at least 12 months. Subjectively, family members observed improved verbal and non-verbal communication.OT produced a sustained, beneficial treatment effect in PPA and warrants further clinical and basic research to identify explanatory factors.

The question of whether to screen for dementia and Alzheimer’s disease (AD) has been discussed in many forums throughout the world. Generally, medical advisory groups and policy-making groups have recognized the importance of early diagnosis but have uniformly avoided making recommendations to screen at-risk populations. This presentation reflects the support for reconsidering the importance of screening individuals at risk or above a certain age. In this statement, the majority of the authors support the consideration of dementia risk factors in individuals at age 50, with routine yearly screening after 75. Other authors remain concerned that the benefits of treatments of early disease do not yet support a general screening recommendation. These statements are made to encourage progress toward the development of a consensus regarding the widespread institution of screening policy. Accordingly, members of the worldwide scientific community are invited to add their perspective by contributing short commentaries (1500 words) on this subject.

Hypothesis: The thickness of a cortical layer is a composite measure of neuronal, axonal, dendritic, synaptic, and glial numbers and sizes that may relate to the function of a cortical area. Methods: 35 age-specific behaviors with defined cortical localization whose onset lies between birth and 72 months were selected. Each behavior's function localized to one or more of 12 cytoarchitectonic areas (Brodmann areas 4, with homuncular subdivisions for leg, trunk, face, and hand, plus 17, 18, 19, 20, 21, 24, 36, and 37). Data on cortical thickness for each layer of 41 cytoarchitectonic areas (including the 12 above) of the postnatal human cerebral cortex from birth of 72 months were analyzed for general patterns of change. For the 12 cortical areas functionally related to the age-specific behaviors, we searched for layer thickness changes that co-related to when the behaviors began. Results: Without exception, all layers of the 41 cortical areas of the postnatal human cerebral cortex studied develop through a...

Summary Objectives: The aim was to evaluate the potential for monotonicity constraints to bias machine learning systems to learn rules that were both accurate and meaningful. Methods: Two data sets, taken from problems as diverse as screening for dementia and assessing the risk of mental retardation, were collected and a rule learning system, with and without monotonicity constraints, was run on each. The rules were shown to experts, who were asked how willing they would be to use such rules in practice. The accuracy of the rules was also evaluated. Results: Rules learned with monotonicity constraints were at least as accurate as rules learned without such constraints. Experts were, on average, more willing to use the rules learned with the monotonicity constraints. Conclusions: The analysis of medical databases has the potential of improving patient outcomes and/or lowering the cost of health care delivery. Various techniques, from statistics, pattern recognition, machine learning, and neural networks, have been proposed to “mine” this data by uncovering patterns that may be used to guide decision making. This study suggests cognitive factors make learned models coherent and, therefore, credible to experts. One factor that influences the acceptance of learned models is consistency with existing medical knowledge.

Forests of synthetic neurons can be generated in graphical form that are both visually and statistically indistinguishable from equivalent populations of biological neurons selected by cytoarchitectural area, layer, age, and neurological diagnosis from the developing human cerebral cortex. Representative populations of several neuron types (pyramidal, spiny stellate, basket cells, chandelier cells, etc.) have been modeled. The long-range goal of these studies, anatomical realism, transcends the traditional statistical view that models the neuron database exclusively as a set of parameters and their variances. Anatomical realism requires that sufficient parameters be captured that a credible forest of neurons can be grown synthetically within the computer and displayed graphically.

Connectivity between 30 cytoarchitectural areas covering almost the whole region of neocortex has been proposed. The data source provided by J.L. Conel was used for the analyses. The analyses consisted of computing correlation coefficients between each cortical layer of one cortical area to each cortical layer of another cortical area over the eight age-points and comparison of the time of the first appearance of myelinated axons per layer per area. Derived connectivity supports some interesting findings reported recently by others and is in good agreement with neural network model deduced from neuroimaging and psychological experiments.

Geometric models of local cortical microstructure can be used to develop neuroanatomically based network models. First, neuron/fiber spatial distributions and neuron morphology, drawing on a new microstructure imaging technology, must be quantified within the cortical tissue. Given this database, stochastic models recreating the spatial distribution and morphology of the observed neurons are constructed. Synthetic neuron forests are then stochastically generated. Neurite near-collisions (e.g., between axons and dendrites) are modeled to add synaptic connections. The synapse estimation complements the forest of synthetic neurons by adding local connectivity, i.e., wiring up the forest into network models.

Cortical connectivity for human cerebral cortex was estimated for 31 cortical areas using microscopic anatomical data of the number of neurons, myelinated axon density, and dendrite fiber density. Estimated connections agree with known connections derived from imaging and tracing studies. Also some unusual connections, which have been recently confirmed by other studies, were estimated.

From 1939 to 1967, J.L. Conel quantitatively studied the microscopic features of the developing human cerebral cortex and published the findings in eight volumes. We have constructed a database using his neuroanatomical measurements (neuronal packing density, myelinated large fiber density, large proximal dendrite density, somal breadth and height, and total cortical and cortical layer thickness) at the eight age periods (0 [term birth], 1, 3, 6, 15, 24, 48, and 72 postnatal months) he studied. In this report, we examine changes in neuron numbers over the eight age-points for 35 von Economo areas for which Conel gave appropriate data. From birth to 3 months postnatal age, total cortical neuron number increases 23–30%, then falls to within 3.5% of the birth value at 24 months, supporting our previous work showing that the observed decrease in the number of neurons per column of cortex under a 1-mm2 cortical surface from birth to 15 months is almost entirely due to cortical surface expansion. The present study also shows a 60–78% increase in total cortical neuron number above the birth value from postnatal ages 24 to 72 months. The generalization, to humans at least, of the finding of no postnatal neurogenesis in rhesus macaques, a species belonging to a super-family that diverged from that of Homo sapiens more than 25 million years ago, is not warranted until explicitly proven for humans. The data of the present study support the existence of substantial postnatal neurogenesis in humans for the 35 cortical areas studied.

We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia staging. Dementia staging in clinical settings is at present complex and highly subjective because of the ambiguities and the complicated nature of existing guidelines. Our model abstracts the two-stage process used by physicians to arrive at the global Clinical Dementia Rating Scale (CDRS) score. The model incorporates learning intermediate concepts (CDRS category scores) in the first stage that then become the feature space for the second stage (global CDRS score). The sample consisted of 678 patients evaluated in the Alzheimer's Disease Research Center at the University of California, Irvine. The demographic variables, functional and cognitive test results used by physicians for the task of dementia severity staging were used as input to the machine learning algorithms. Decision tree learners and rule inducers (C4.5, Cart, C4.5 rules) were selected for our study as they give expressive models, and Naive Bayes was used as a baseline algorithm for comparison purposes. We first learned the six CDRS category scores (memory, orientation, judgement and problem solving, personal care, home and hobbies, and community affairs). These learned CDRS category scores were then used to learn the global CDRS scores. The Two-Stage ML model classified as well as or better than the published inter-rater agreements for both the category and global CDRS scoring by dementia experts. Furthermore, for the most critical distinction, normal versus very mildly impaired, the Two-Stage ML model was 28.1 and 6.6% more accurate than published performances by domain experts. Our study of the CDRS examined one of the largest, most diverse samples in the literature, suggesting that our findings are robust. The Two-Stage ML model also identified a CDRS category, Judgment and Problem Solving, which has low classification accuracy similar to published reports. Since this CDRS category appears to be mainly responsible for misclassification of the global CDRS score when it occurs, further attribute and algorithm research on the Judgment and Problem Solving CDRS score could improve its accuracy as well as that of the global CDRS score.

Dipolarity is the goodness-of-fit of the observed potential distribution with one calculated using specific assumptions about the source of the electrical potential distribution. The authors used computer simulations to examine the effect of different distributions of sources on their resulting dipolarity values. Electric dipoles were placed in a head-shaped model with uniform conductivity using four different dipole configurations (randomly oriented dipoles, a uniform dipole disk layer, a dipole disk layer with uniformly distributed holes, or one with randomly oriented dipoles). The best-fitting single dipole for each configuration was calculated and the dipolarity was computed as the mean squared error of the electrical potential distributions generated by the actual dipole configuration and by the best-fitting single dipole. The simulations show that: 1) a smooth dipole layer with or without holes gives dipolarities above 99.5% even when extended over areas as large as 1256 mm/sup 2/; 2) randomly oriented dipoles under a smooth dipole layer also give dipolarities above 99.5%; and 3) randomly oriented and distributed dipoles, even if contained in a small portion of the total area, give dipolarities below 93.0%. These simulations show that inhomogeneity (holes) within a dipole disk layer per se do not lower dipolarity; rather, it is the random orientation and distribution of these dipoles which lowers dipolarity. Furthermore, dipolarity is not lowered by such randomly oriented and distributed dipoles when they are beneath a dipole disk layer.

This study examines JL Conel's data on neuron numbers in 35 human cortical areas for eight age points from 0 (birth) to 72 months, to analyze cortical columns, the presumed functional units of the cortex. For each cortical area at each age point, cortical surface divided by the square root of the area's neuron number gives cross-sectional areas with radii ranging from 180 microns at birth to 250 microns at 72 months. For the prefrontal cortex at birth and 48 months, these radii are approximately 2.10 and 1.19 times the longest radial basal dendrites, suggesting similar dimensions between these two measures of column radius. The logarithm of neuron number per cortical area and age point was examined in relation to the Weber-Fechner law governing the relationship between stimulus intensity and perception. A mechanism for this law consistent with the cortical model of Douglas et al. illustrates the importance of local circuit neurons. The cross-sectional areas of hexagonal columns for prefrontal cortex, using as radius, the longest radial extent of layer 5 pyramidal neuron basal dendrites, ranging from 0.013 mm2 at birth to 0.064 mm2 at 48 months, suggests that functional cortical columns increase cross-sectional area during development. These cross-sectional areas are 55-100-fold larger at birth, and 229-277-fold larger at 48 months, than those computed from somal width in prefrontal, layer 5 pyramidal neurons. Comparison of radial extent of pyramidal basal dendrites to their soma-to-soma distances shows that layer 3 pyramidal basal dendrites reach 1.5 and 4.0 other pyramidal neurons at 15 and 60 months, respectively, while layer 5, extra-large pyramidal basal dendrites reach 1.14 and 1.72 other such neurons at birth and 48 months, respectively. If such a relationship holds for other cortical areas, then the Conel data can be used to estimate basal dendrite extent, for which there currently is a paucity of data.

In this study, we searched for patterns in selected, quantitative microscopic features of the developing postnatal human cortex for 35 cytoarchitectural areas at eight age points from birth to 72 months. These data come from the largest extant survey of the microscopic features of the developing postnatal human cerebral cortex (JL Conel, 1939-67). In contrast to Jacobson's proposal that cortical surface area increases in proportion to brain weight, Conel's data show that surface area increases as brain weight2/3, with the maximal rate of increase in both brain weight and cortical surface area occurring from 1 to 3 months. We computed the numbers of cortical neurons per cortical layer under 1 mm2 of cortical surface (neurons/layer per mm2 column) and divided these values by the total neuron number/mm2 column. For all areas, these data show plateaux in most of the layers for periods of months to years, often preceded and followed by changes in their neuron number in a sinusoidal fashion. The age points of the maxima and minima of such laminar values differ across the 35 cortical areas, indicating that their sinusoids are phase-shifted with respect to each other. Ranking the six layers in each area at each age point by their neuron number/layer per mm2 column shows that, by 72 months, the first areas to receive thalamic auditory or visual input (primary sensory and unimodal association cortices) have the most neurons in layer 4 and in either layer 3 or 6. In contrast, by 72 months, other areas have the most neurons in layers 3 and 6, with the primary motor cortex reaching this ranking earlier than any other area. For temporal and parietal association areas, layers 2 (short cortico-cortical connections) and 4 (thalamo-cortical connections) have the most neurons from birth to 6 months, whereas layers 3 (long cortico-cortical connections) and 6 (cortico-thalamic connections) have the most neurons by 72 months. The quantitative, statistically non-random patterns demonstrated by our analyses suggest that hierarchical correlations between such structural changes and age-specific behavioral acquisitions exist during the first 72 months of postnatal development.

This paper uses correspondence analysis to examine the developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to 72 months. The study is based on data collected by the late J. L. Conel, which consist of over 4 million individual measurements of six microscopic neuroanatomic features for each of six cortical layers in 46 cytoarchitecturally distinct regions. We analyze 1,727 profiles of development over eight age-points (term birth, 1, 3, 6, 15, 24, 48, and 72 postnatal months) resulting from the combinations of neuroanatomic feature, cortical layer, and brain cytoarchitectural region in the Conel data. The profiles for any given combination of feature and layer are found to be remarkably similar in all regions of the brain, and therefore the developmental patterns of different cytoarchitectural regions are not distinguishable from one another. Developmental change is most rapid at the earlier stages; of the total change in profile patterns observed, more than one-third occurs between birth and 6 months, about one-third occurs between 6 and 15 months, and less than one-third occurs between 15 and 72 months. The majority of the variance in developmental profiles is accounted for by the six microscopic, neuroanatomic features. Correspondence analysis shows that Conel’s data are highly consistent and reliable.

Counts of total neuron number per section and of neurons per microscopic field of inferior olivary principal nuclei were made on sections from 10 patients with Down syndrome (DS) aged 0.36 to 28 months and seven control (C) patients aged 1 to 29 months. After stereologic and appropriate shrinkage corrections of the count data, the ratios of values for DS/C were calculated. For mean principal olivary nucleus neuron number, DS/C = 0.64; for mean number of neurons per field, DS/C = 0.84; for mean volume of olivary neuronal band per section, DS/C = 0.79; and for mean volume of neuronal band per neuron, DS/C =1.27. The data are in accord with other data suggesting that (1) numbers of cells in various cell populations, including various areas of the cerebrum, in DS approximate two-thirds normal (DS/C ≈ 0.67); (2) for the volumes of such cell populations, DS/C ≈ 0.82 normal; and (3) for volumes of individual cells, DS/C ≈ 1.22 normal. The data of the present study suggest that the inferior olivary nuclei in DS a...