Paving the Way for Alzheimer's Disease Prevention: A Systematic Review of Global Open-Access Neuroimaging Datasets in Healthy Individuals.

BACKGROUND: Given the advent of large-scale neuroimaging data-driven endeavors for Alzheimer's disease, there is a burgeoning need for well-characterized neuroimaging databases of healthy individuals. With the rise of initiatives around the globe for the rapid and unrestricted sharing of data resources, there is now an abundance of open-source neuroimaging datasets available to the research community. However, there is not yet a systematic review that fully details the demographic information and modalities actually available in all open access neuroimaging databases around the globe. OBJECTIVE: This systematic review aims to provide compile a list of MR structural imaging databases encompassing healthy individuals across the lifespan. METHODS: In this systematic review, we searched EMBASE and PubMed until May 2022 for open-access neuroimaging databases containing healthy control participants of any age, race, with normal development and cognition having at least one structural T1-weighted neuroimaging scan. RESULTS: A total of 403 databases were included, for up to total of 48,268 participants with all available demographic information and imaging modalities detailed in Supplementary Table 1. There were significant trends noted when compiling normative databases for this systematic review, notably that 11.7% of databases included reported ethnicity in their participants, with underrepresentation of many socioeconomic groups globally. CONCLUSIONS: As efforts to improve primary prevention of AD may require a broader perspective including increased relevance of earlier stages in life, and strategies in addressing modifiable risk factors may be individualized to specific demographics, improving data characterization to be richer and more rigorous will greatly enhance these efforts.

Low thalamic activity during a digit-symbol substitution task is associated with symptoms of subjective cognitive decline.

Introduction: Subjective cognitive decline (SCD) may represent the earliest preclinical stage of Alzheimer's Disease (AD) for some older adults. However, the underlying neurobiology of SCD is not completely understood. Since executive function may be affected earlier than memory function in the progression of AD, we aimed to characterize SCD symptoms in terms of fMRI brain activity during the computerized digit-symbol substitution task (DSST), an executive function task. We also explored associations of DSST task performance with brain activation, SCD severity, and amyloid-ß (Aß) load. Methods: We analyzed data from 63 cognitively normal older individuals (mean age 73.6 ± 7.2) with varying degree of SCD symptoms. Participants completed a computerized version of DSST in the MR scanner and a Pittsburgh Compound-B (PiB)-PET scan to measure global cerebral Aß load. Results: A voxel-wise analysis revealed that greater SCD severity was associated with lower dorsomedial thalamus activation. While task performance was not associated with brain activation nor Aß load, slower reaction time was associated with greater SCD severity. Discussion: The observed lower dorsomedial thalamus activation may reflect declining familiarity-based working memory and the trans-thalamic executive function pathway in SCD. SCD symptoms may reflect altered neural function and subtle decline of executive function, while Aß load may have an indirect impact on neural function and performance. Self-perceived cognitive decline may serve as a psychological/subjective marker reflecting subtle brain changes.

Alzheimer Disease Pathology and Neurodegeneration in Midlife Obesity: A Pilot Study.

Obesity and excess adiposity at midlife are risk factors for Alzheimer disease (AD). Visceral fat is known to be associated with insulin resistance and a pro-inflammatory state, the two mechanisms involved in AD pathology. We assessed the association of obesity, MRI-determined abdominal adipose tissue volumes, and insulin resistance with PET-determined amyloid and tau uptake in default mode network areas, and MRI-determined brain volume and cortical thickness in AD cortical signature in the cognitively normal midlife population. Thirty-two middle-aged (age: 51.27±6.12 years, 15 males, body mass index (BMI): 32.28±6.39 kg/m2) cognitively normal participants, underwent bloodwork, brain and abdominal MRI, and amyloid and tau PET scan. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi-automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer was used to automatically segment brain regions using a probabilistic atlas. PET scans were acquired using [11C]PiB and AV-1451 tracers and were analyzed using PET unified pipeline. The association of brain volumes, cortical thicknesses, and PiB and AV-1451 standardized uptake value ratios (SUVRs) with BMI, VAT/SAT ratio, and insulin resistance were assessed using Spearman's partial correlation. VAT/SAT ratio was associated significantly with PiB SUVRs in the right precuneus cortex (p=0.034) overall, controlling for sex. This association was significant only in males (p=0.044), not females (p=0.166). Higher VAT/SAT ratio and PiB SUVRs in the right precuneus cortex were associated with lower cortical thickness in AD-signature areas predominantly including bilateral temporal cortices, parahippocampal, medial orbitofrontal, and cingulate cortices, with age and sex as covariates. Also, higher BMI and insulin resistance were associated with lower cortical thickness in bilateral temporal poles. In midlife cognitively normal adults, we demonstrated higher amyloid pathology in the right precuneus cortex in individuals with a higher VAT/SAT ratio, a marker of visceral obesity, along with a lower cortical thickness in AD-signature areas associated with higher visceral obesity, insulin resistance, and amyloid pathology.

Neuroinflammation: A Modifiable Pathway Linking Obesity, Alzheimer's disease, and Depression.

Obesity, depression and Alzheimer's disease (AD) are three major interrelated modern health conditions with complex relationships. Early-life depression may serve as a risk factor for AD, while late-life depression may be a prodrome of AD. Depression affects approximately 23% of obese individuals, and depression itself raises the risk of obesity by 37%. Mid-life obesity independently increases AD risk, while late-life obesity, particularly metabolically healthy obesity, may offer protection against AD pathology. Chronic inflammation serves as a key mechanism linking obesity, AD, and depression, encompassing systemic inflammation from metabolic disturbances, immune dysregulation through the gut microbiome, and direct interactions with amyloid pathology and neuroinflammation. In this review, we explore the biological mechanisms of neuroinflammation in relation to obesity, AD, and depression. We assess the efficacy of therapeutic interventions targeting neuroinflammation and discuss current and future radiological imaging initiatives for studying neuroinflammation. By comprehending the intricate interplay among depression, obesity, and AD, especially the role of neuroinflammation, we can advance our understanding and develop innovative strategies for prevention and treatment.

"Brain age" predicts disability accumulation in multiple sclerosis.

OBJECTIVE: Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset. METHODS: Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. RESULTS: MS was associated with advanced predicted brain age cross-sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. INTERPRETATION: Brain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.

Brain PET Imaging: Frontotemporal Dementia.

Brain PET adds value in diagnosing neurodegenerative disorders, especially frontotemporal dementia (FTD) due to its syndromic presentation that overlaps with a variety of other neurodegenerative and psychiatric disorders. 18F-FDG-PET has improved sensitivity and specificity compared with structural MR imaging, with optimal diagnostic results achieved when both techniques are utilized. PET demonstrates superior sensitivity compared with SPECT for FTD diagnosis that is primarily a supplement to other imaging and clinical evaluations. Tau-PET and amyloid-PET primary use in FTD diagnosis is differentiation from Alzheimer disease, although these methods are limited mainly to research settings.

Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction.

We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49-89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06-1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76-3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33-2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44-3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43-4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

Soluble epoxide hydrolase inhibitor can protect the femoral head against tobacco smoke exposure-induced osteonecrosis in spontaneously hypertensive rats.

Exposure to tobacco smoke (TS) has been considered a risk factor for osteonecrosis of the femoral head (ONFH). Soluble epoxide hydrolase inhibitors (sEHIs) have been found to reduce inflammation and oxidative stress in a variety of pathologies. This study was designed to assess the effect of sEHI on the development of ONFH phenotypes induced by TS exposure in spontaneously hypertensive (SH) rats. SH and normotensive Wistar Kyoto (WKY) rats were exposed to filtered air (FA) or TS (80 mg/m3 particulate concentration) 6 h/day, 3 days/week for 8 weeks. During this period, sEHI was delivered through drinking water at a concentration of 6 mg/L. Histology, immunohistochemistry, and micro-CT morphometry were performed for phenotypic evaluation. As results, TS exposure induced significant increases in adipocyte area, bone specific surface (BS/BV), and trabecular separation (Tb.SP), as well as significant decreases in bone mineral density (BMD), percent trabecular area (Tb.Ar), HIF-1a expression, bone volume fraction (BV/TV), trabecular numbers (Tb.N), and trabecular thickness (Tb.Th) in both SH and WKY rats. However, the protective effects of sEHI were mainly observed in TS-exposed SH rats, specifically in the density of osteocytes, BMD, Tb.Ar, HIF-1a expression, BV/TV, BS/BV, Tb.N, and Tb.SP. Our study confirms that TS exposure can induce ONFH especially in SH rats, and suggests that sEHI therapy may protect against TS exposure-induced osteonecrotic changes in the femoral head.

An Effect of Education on Memory-Encoding Activation in Subjective Cognitive Decline.

BACKGROUND: Subjective cognitive decline (SCD) may be an early manifestation of pre-clinical Alzheimer's disease. Elevated amyloid-ß (Aß) is a correlate of SCD symptoms in some individuals. The underlying neural correlates of SCD symptoms and their association with Aß is unknown. SCD is a heterogeneous condition, and cognitive reserve may explain individual differences in its neural correlates. OBJECTIVE: We investigated the association between brain activation during memory encoding and SCD symptoms, as well as with Aß, among older individuals. We also tested the moderating role of education (an index of cognitive reserve) on the associations. METHODS: We measured brain activation during the "face-name" memory-encoding fMRI task and Aß deposition with Pittsburgh Compound-B (PiB)-PET among cognitively normal older individuals (n = 63, mean age 73.1 ± 7.4 years). We tested associations between activation and SCD symptoms by self-report measures, Aß, and interactions with education. RESULTS: Activation was not directly associated with SCD symptoms or Aß. However, education moderated the association between activation and SCD symptoms in the executive control network, salience network, and subcortical regions. Greater SCD symptoms were associated with greater activation in those with higher education, but with lower activation in those with lower education. CONCLUSION: SCD symptoms were associated with different patterns of brain activation in the extended memory system depending on level of cognitive reserve. Greater SCD symptoms may represent a saturation of neural compensation in individuals with greater cognitive reserve, while it may reflect diminishing neural resources in individuals with lower cognitive reserve.

Aging faster: worry and rumination in late life are associated with greater brain age.

Older adults with anxiety have lower gray matter brain volume-a component of accelerated aging. We have previously validated a machine learning model to predict brain age, an estimate of an individual's age based on voxel-wise gray matter images. We investigated associations between brain age and anxiety, depression, stress, and emotion regulation. We recruited 78 participants (≥50 years) along a wide range of worry severity. We collected imaging data and computed voxel-wise gray matter images, which were input into an existing machine learning model to estimate brain age. We conducted a multivariable linear regression between brain age and age, sex, race, education, worry, anxiety, depression, rumination, neuroticism, stress, reappraisal, and suppression. We found that greater brain age was significantly associated with greater age, male sex, greater worry, greater rumination, and lower suppression. Male sex, worry, and rumination are associated with accelerated aging in late life and expressive suppression may have a protective effect. These results provide evidence for the transdiagnostic model of negative repetitive thoughts, which are associated with cognitive decline, amyloid, and tau.

Obesity and White Matter Neuroinflammation Related Edema in Alzheimer's Disease Dementia Biomarker Negative Cognitively Normal Individuals.

BACKGROUND: Obesity is related to quantitative neuroimaging abnormalities including reduced gray matter volumes and impaired white matter microstructural integrity, although the underlying mechanisms are not well understood. OBJECTIVE: We assessed influence of obesity on neuroinflammation imaging that may mediate brain morphometric changes. Establishing the role of neuroinflammation in obesity will enhance understanding of this modifiable disorder as a risk factor for Alzheimer's disease (AD) dementia. METHODS: We analyzed brain MRIs from 104 cognitively normal participants (CDR = 0) and biomarker negativity for CSF amyloid or tau. We classified body mass index (BMI) as normal (BMI <25, N = 62) or overweight and obese (BMI ≥25, N = 42). Blood pressure was measured. BMI and blood pressure classifications were related to neuroinflammation imaging (NII) derived edema fraction in 17 white matter tracts. This metric was also correlated to hippocampal volumes and CSF biomarkers of inflammation and neurodegeneration: YKL-40, SNAP25, VILIP, tau, and NFL. RESULTS: Participants with BMI <25 had lower NII-derived edema fraction, with protective effects of normal blood pressure. Statistically significant white matter tracts included the internal capsule, external capsule, and corona radiata, FDR correc-ted for multiple comparisons to alpha = 0.05. Higher NII-derived edema fractions in the internal capsule, corpus callosum, gyrus, and superior fronto-occipital fasciculus were related with smaller hippocampal volumes only in individuals with BMI ≥25. There were no statistically significant correlations between NII-derived edema fraction and CSF biomarkers. CONCLUSION: We demonstrate statistically significant relationships between neuroinflammation, elevated BMI, and hippocampal volume, raising implications for neuroinflammation mechanisms of obesity-related brain dysfunction in cognitively normal elderly.

Accelerated brain aging in chronic low back pain.

Chronic low back pain (CLBP) is a leading cause of disability and is associated with neurodegenerative changes in brain structure. These changes lead to impairments in cognitive function and are consistent with those seen in aging, suggesting an accelerated aging pattern. In this study we assessed this using machine-learning estimated brain age (BA) as a holistic metric of morphometric changes associated with aging. Structural imaging data from 31 non-depressed CLBP patients and 32 healthy controls from the Pain and Interoception Imaging Network were included. Using our previously developed algorithm, we estimated BA per individual based on grey matter density. We then conducted multivariable linear modeling for effects of group, chronological age, and their interaction on BA. We also performed two voxel-wise analyses comparing grey matter density between CLBP and control individuals and the association between gray matter density and BA. There was an interaction between CLBP and greater chronological age on BA such that the discrepancy in BA between healthy and CLBP individuals was greater for older individuals. In CLBP individuals, BA was not associated with sex, current level of pain, duration of CLBP, or mild to moderate depressive symptoms. CLBP individuals had lower cerebellar grey matter density compared to healthy individuals. Brain age was associated with lower gray matter density in numerous brain regions. CLBP was associated with greater BA, which was more profound in later life. BA as a holistic metric was sensitive to differences in gray matter density in numerous regions which eluded direct comparison between groups.

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus.

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

The effect of amyloid deposition on longitudinal resting-state functional connectivity in cognitively normal older adults.

BACKGROUND: Pathological processes contributing to Alzheimer's disease begin decades prior to the onset of clinical symptoms. There is significant variation in cognitive changes in the presence of pathology, functional connectivity may be a marker of compensation to amyloid; however, this is not well understood. METHODS: We recruited 64 cognitively normal older adults who underwent neuropsychological testing and biannual magnetic resonance imaging (MRI), amyloid imaging with Pittsburgh compound B (PiB)-PET, and glucose metabolism (FDG)-PET imaging for up to 6 years. Resting-state MRI was used to estimate connectivity of seven canonical neural networks using template-based rotation. Using voxel-wise paired t-tests, we identified neural networks that displayed significant changes in connectivity across time. We investigated associations among amyloid and longitudinal changes in connectivity and cognitive function by domains. RESULTS: Left middle frontal gyrus connectivity within the memory encoding network increased over time, but the rate of change was lower with greater amyloid. This was no longer significant in an analysis where we limited the sample to only those with two time points. We found limited decline in cognitive domains overall. Greater functional connectivity was associated with better attention/processing speed and executive function (independent of time) in those with lower amyloid but was associated with worse function with greater amyloid. CONCLUSIONS: Increased functional connectivity serves to preserve cognitive function in normal aging and may fail in the presence of pathology consistent with compensatory models.

Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease.

Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD). We showed that amyloid status is a critical feature for brain age prediction models. We trained a model on T1-weighted MRI images participants without amyloid pathology. MRI data were processed to estimate gray matter density voxel-wise, which were then used to predict chronological age. Our model performed accurately comparable to previous models. Notably, we demonstrated more significant differences between AD diagnostic groups than other models. In addition, our model was able to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid. Incorporation of amyloid status in brain age prediction models ultimately improves the utility of brain age as a biomarker for AD.

Overview of MR Imaging Volumetric Quantification in Neurocognitive Disorders.

This review article provides a general overview on the various methodologies for quantifying brain structure on magnetic resonance images of the human brain. This overview is followed by examples of applications in Alzheimer dementia and mild cognitive impairment. Other examples will include traumatic brain injury and other neurodegenerative dementias. Finally, an overview of general principles for protocol acquisition of magnetic resonance imaging for volumetric quantification will be discussed along with the current choices of FDA cleared algorithms for use in clinical practice.

A Homeostatic Model of Subjective Cognitive Decline.

Subjective Cognitive Decline (SCD) is possibly one of the earliest detectable signs of dementia, but we do not know which mental processes lead to elevated concern. In this narrative review, we will summarize the previous literature on the biomarkers and functional neuroanatomy of SCD. In order to extend upon the prevailing theory of SCD, compensatory hyperactivation, we will introduce a new model: the breakdown of homeostasis in the prediction error minimization system. A cognitive prediction error is a discrepancy between an implicit cognitive prediction and the corresponding outcome. Experiencing frequent prediction errors may be a primary source of elevated subjective concern. Our homeostasis breakdown model provides an explanation for the progression from both normal cognition to SCD and from SCD to advanced dementia stages.

Advances and Barriers for Clinical Neuroimaging in Late-Life Mood and Anxiety Disorders.

PURPOSE OF REVIEW: Mood and anxiety disorders are very commonly experienced by older adults and are becoming a growing concern due to the rapidly aging global population. Recent advances in neuroimaging may help in improving outcomes in late-life mood and anxiety disorders. The elucidation of mechanisms contributing to late-life mental health disorders may ultimately lead to the identification of novel therapeutic interventions. Alternatively, clinically validated imaging biomarkers may allow for the prediction of treatment response and identification of better therapeutic approaches in late-life mood and anxiety disorders. RECENT FINDINGS: In community samples, late-life depression and late-life generalized anxiety disorder occur up to 38 and 15%, respectively, while late-life bipolar disorder is less common and occur in approximately 0.5% of the population. There are significant challenges in treating and improving outcome in late-life mood and anxiety disorders. Time to treatment response and treatment resistance are increased in older adults. Novel neuroimaging techniques have the potential to improve diagnostic and therapeutic outcome in late-life mood and anxiety disorders either through "personalized pharmacotherapy" or through identifying dysfunction regions/networks to be subsequently used for direct interventions such as transcranial magnetic stimulation. This review will provide an overview of recent literature that substantiates the potential role of neuroimaging in clinical practice, as well as the barriers that must be overcome prior to clinical translation.

Brain imaging in the differential diagnosis of young-onset dementias.

Young-onset dementia is a broad category of diseases that affect adults before the age of 65, with devastating effects on individuals and families. Neuroimaging plays a clear and ever-expanding role in the workup of these diseases. MRI demonstrates classic patterns of atrophy that help to confirm the clinical diagnosis and may predict the underlying disease. Functional nuclear imaging, such as PET, demonstrates areas of brain dysfunction even in the absence of visible atrophy. These techniques can inform important aspects of the care of young-onset dementia, such as the underlying pathologic condition, treatment, and prognosis.

Dissociated signals in human dentate gyrus and CA3 predict different facets of recognition memory.

A wealth of evidence has implicated the hippocampus and surrounding medial temporal lobe cortices in support of recognition memory. However, the roles of the various subfields of the hippocampus are poorly understood. In this study, we concurrently varied stimulus familiarization and repetition to engage different facets of recognition memory. Using high-resolution fMRI (1.5 mm isotropic), we observed distinct familiarity and repetition-related recognition signal profiles in the dentate gyrus (DG)/CA3 subfield in human subjects. The DG/CA3 demonstrated robust response suppression with repetition and familiarity-related facilitation. Both of these discrete responses were predictive of different aspects of behavioral performance. Consistent with previous work, we observed novelty responses in CA1 consistent with "match/mismatch detection," as well as mixed recognition signaling distributed across medial temporal lobe cortices. Additional analyses indicated that the repetition and familiarity-related signals in the DG/CA3 were strikingly dissociated along the hippocampal longitudinal axis and that activity in the posterior hippocampus was strongly correlated with the retrosplenial cortex. These data provide novel insight into the roles of hippocampal subfields in support of recognition memory and further provide evidence of a functional heterogeneity in the human DG/CA3, particularly along the longitudinal axis.