Medial Temporal Cortex Research Articles

Resting-state fMRI is associated with trauma experiences, mood and psychosis in Afro-descendants with bipolar disorder and schizophrenia

BackgroundBipolar disorder (BD) and schizophrenia (SCZ) may exhibit functional abnormalities in several brain areas, including the medial temporal and prefrontal cortex and hippocampus; however, a less explored topic is how brain connectivity is linked to premorbid trauma experiences and clinical features in non-Caucasian samples of SCZ and BD. Methods: Sixty-two individuals with SCZ (n = 20), BD (n = 21), and healthy controls (HC, n = 21) from indigenous and African ethnicity were submitted to clinical screening (Di-PAD), traumata experiences (ETISR-SF), cognitive and functional MRI assessment. The item psychosis/hallucinations in SCZ patients showed a negative correlation with the global efficiency (GE) in the right dorsal attention network. The items mania, irritable mood, and racing thoughts in the Di-PAD scale had a significant negative correlation with the GE in the parietal right default mode network. Conclusions: Differences in the activation of specific networks were associated with earlier disease onset, history of physical abuse, and more severe psychotic and mood symptoms in SCZ and BD subjects of indigenous and black ethnicity. Findings provide further evidence on SZ and BD's brain connectivity disturbances, and their clinical significance, in non-Caucasian samples.

Previous pregnancies might mitigate cortical brain differences associated with surgical menopause.

Surgical menopause causes a sharp drop in estrogen levels in middle-aged women, thus preventing the gradual physiological adaptation that is characteristic of the perimenopause. Previous studies suggest that surgical menopause might increase the risk of dementia later in life. In addition, the transition to motherhood entails long-lasting endocrine and neuronal adaptations. We compared differences in whole-brain cortical volume between women who reached menopause by surgery and a group of women who reached spontaneous non-surgical menopause and determined whether these cortical differences were influenced by previous childbearing. Using surface-based neuroimaging techniques, we investigated cortical volume differences in 201 middle-aged women (134 women who experienced non-surgical menopause, 78 of whom were parous women; and 67 women who experienced surgical menopause, 39 of whom were parous women). We found significant atrophy in the frontal and temporal regions in women who experienced surgical menopause. Nulliparous women with surgical menopause showed significant lower cortical volume in the left temporal gyrus extending to the medial temporal lobe cortex, as well as in the precuneus bilaterally compared to parous women with surgical menopause; whereas our results revealed no significant differences between parous women with surgical menopause and both parous and nulliparous women who reached a non-surgical menopause. Furthermore, in the surgical menopause group, we found a negative correlation between cortical volume and age at first pregnancy in the temporal lobe. Our study suggests that the long-term brain remodeling of parity may mitigate the neural impact of the sudden drop in estrogen levels that characterizes surgical menopause.

Individual‐specific change in Alzheimer’s disease vulnerable brain regions across the healthy adult lifespan is associated with genetic AD risk

AbstractBackgroundAcross the healthy adult lifespan, our brain’s undergo widespread structural change. Many of these changes – occurring gradually across life – are not qualitatively distinct from atrophy patterns seen in Alzheimer’s disease (AD), raising the possibility accelerated longitudinal brain change across healthy adult life may relate to genetic AD‐risk.MethodWe modelled subject‐specific structural brain change relative to that expected given age, in dense longitudinal adult lifespan data (1430 scans from 420 individuals aged 30 to 89 years; 2‐7 timepoints). Using polygenic AD scores (PRS‐AD) from four GWAS, we tested PRS‐AD associations with age‐relative change in early Braak stage regions – namely hippocampus, entorhinal cortex, amygdala, and medial temporal cortex. Next, to empirically identify features with accelerated change in AD, in ADNI data (scans = 4410; N = 978) we applied machine learning classification on the subject‐specific slopes estimated from individuals classed as consistently healthy over time versus those succumbing to AD. In healthy adult lifespan data, we then tested PRS‐AD associations with age‐relative change, in many combinations of brain features with model‐implied importance. Lastly, we tested whether high PRS‐AD individuals showing more negative brain change exhibit more longitudinal memory decline over healthy adult life.ResultIn healthy adult lifespan data, we observed many significant PRS‐AD associations with age‐relative change in hippocampus (bilateral), right entorhinal cortex, amygdala (bilateral), and left medial temporal cortex, and typically for all four scores (FDR‐corrected). We show that age‐relative change across the healthy adult lifespan in many combinations of AD‐accelerated features is significantly associated with PRS‐AD. As proof‐of‐principle, applying the ADNI‐derived model weights to the healthy adult lifespan data also enabled detection of PRS‐AD associations in healthy adults, but only using models trained on age‐relative change. Most, but not all, of the observed PRS‐AD associations were driven by APOE. Finally, we find high PRS‐AD individuals with more negative brain change exhibit significantly more memory decline, relative to high PRS‐AD individuals with less brain change.ConclusionThe results support a dimensional account linking gradual lifespan brain changes with pathological aging, and demonstrate PRS‐AD impacts the slope of brain aging in most AD‐relevant brain regions in healthy adults.

Which Convolutional Neural Network Architecture is the Best at Detecting Alzheimer’s Disease: A head‐to‐head Comparison Using Relevance Maps

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder which causes gradual and irreversible damage to the brain. These patterns of brain damage can be detected using T1‐weighted MRI scans by modern neuroimaging methods. Recent developments in convolutional neural networks (CNN) have achieved promising results in various computer‐vision tasks. Feature attribution methods such as layer‐wise relevance propagation (LRP) allow tracing back the information flow in CNNs to derive relevance heatmaps, which approximate the contribution of the input image regions on the model decision. We addressed the open question, which of the most common CNN architectures is best suited for medical image detection, i.e. AD classification based on MRI data.MethodFour CNN architectures are widely used in the literature: AlexNet, VGG, ResNet, and DenseNet. We adapted these CNN architectures to be used with 3D brain MRI data and trained the models on a heterogeneous dataset with N>2200 from four large studies. We applied tenfold cross‐validation and additionally evaluated the results on an independent test dataset.ResultThe more complex CNN architectures DenseNet and ResNet with ‘skip connections’, provided the best results for both group separation tasks of ‐ AD vs Cognitively Normal (CN), and Mild Cognitive Impairment (MCI) vs CN, although the overall differences in accuracy did not reach statistical significance. From the mean relevance maps obtained using the LRP relevance propagation method (see Fig. 1.), we found that DenseNet focused primarily on the medial temporal lobe and posterior cingulate cortex atrophy. ResNet’s mean relevance maps were more noisy and heterogeneous.ConclusionDenseNet provided the most focused relevance maps, best matching apriori expectations of brain regions contributing to the detection of AD. Its ‘dense connections’ between the layers enabled a highly efficient information flow at various scales. Our evaluation setup also demonstrated the value of a holistic evaluation of models, where relevance maps are used in combination with classical performance metrics.

Which Convolutional Neural Network Architecture is the Best at Detecting Alzheimer’s Disease: A head‐to‐head Comparison Using Relevance Maps

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder which causes gradual and irreversible damage to the brain. These patterns of brain damage can be detected using T1‐weighted MRI scans by modern neuroimaging methods. Recent developments in convolutional neural networks (CNN) have achieved promising results in various computer‐vision tasks. Feature attribution methods such as layer‐wise relevance propagation (LRP) allow tracing back the information flow in CNNs to derive relevance heatmaps, which approximate the contribution of the input image regions on the model decision. We addressed the open question, which of the most common CNN architectures is best suited for medical image detection, i.e. AD classification based on MRI data.MethodFour CNN architectures are widely used in the literature: AlexNet, VGG, ResNet, and DenseNet. We adapted these CNN architectures to be used with 3D brain MRI data and trained the models on a heterogeneous dataset with N>2200 from four large studies. We applied tenfold cross‐validation and additionally evaluated the results on an independent test dataset.ResultThe more complex CNN architectures DenseNet and ResNet with ‘skip connections’, provided the best results for both group separation tasks of ‐ AD vs Cognitively Normal (CN), and Mild Cognitive Impairment (MCI) vs CN, although the overall differences in accuracy did not reach statistical significance. From the mean relevance maps obtained using the LRP relevance propagation method (see Fig. 1.), we found that DenseNet focused primarily on the medial temporal lobe and posterior cingulate cortex atrophy. ResNet’s mean relevance maps were more noisy and heterogeneous.ConclusionDenseNet provided the most focused relevance maps, best matching apriori expectations of brain regions contributing to the detection of AD. Its ‘dense connections’ between the layers enabled a highly efficient information flow at various scales. Our evaluation setup also demonstrated the value of a holistic evaluation of models, where relevance maps are used in combination with classical performance metrics.

Validating noninvasive techniques for 11C‐ER176 PET quantification in controls and Alzheimer’s disease patients

AbstractBackgroundNoninvasive yet accurate PET quantification methods are crucial for safe yet meaningful PET studies. [11C]ER176 is a PET radioligand that images neuroinflammation via the 18‐kDa translocator protein (TSPO), which is upregulated in activated microglia and has previously been a PET target in Alzheimer’s disease research. Currently, the gold‐standard method for quantifying ER176 PET is kinetic modeling of the dynamic PET data using the arterial input function (AIF), which is invasive and expensive. The current study explores a pseudo‐reference region (RR) technique to noninvasively quantify ER176 scans to facilitate clinical PET studies.MethodDynamic 90‐min [11C]ER176 PET scans were acquired in 11 participants, 5 controls (5M/0F) and 6 patients (3 MCI‐3M/0F; 3 AD‐2M/1F). Arterial blood data were collected throughout the duration of the scan and were used along with the dynamic imaging data to determine regional total volume of distribution (V T) using the two‐tissue compartment model. Standardized Uptake Value Ratio (SUVR) was calculated by normalizing the radioactivity in each region by that of the reference region (cerebellar gray matter). We explored differences across methods (AIF‐derived V T vs. RR‐derived SUVR) and across groups (controls vs. patients).ResultWithin each group, the outcome measures from AIF and RR methods were well correlated (Controls: R2 = 0.986, p < 0.001; Patients: R2 = 0.983, p < 0.001; Figure 1). Both methods showed greater TSPO expression in patients compared to controls, with mean differences in the medial temporal cortex of 14.99% between groups for V T and 10.65% between groups for SUVR, but lower variance for SUVR compared to V T (Figure 2). Both quantification methods showed similar regional patterns of percent differences between groups, corresponding to tau progression along Braak staging (Figure 3).ConclusionResults suggest that using the cerebellum, which does not accumulate amyloid plaques or tau aggregates until end‐stage disease, as a reference region for [11C]ER176 may be a valid method for quantifying differences between AD patients and controls compared to gold‐standard methodology. Regional increases in neuroinflammation are more reliable for SUVR than V T in AD‐related regions. Ongoing work includes a larger sample size to further validate noninvasive PET quantification.

Validating noninvasive techniques for 11C‐ER176 PET quantification in controls and Alzheimer’s disease patients

AbstractBackgroundNoninvasive yet accurate PET quantification methods are crucial for safe yet meaningful PET studies. [11C]ER176 is a PET radioligand that images neuroinflammation via the 18‐kDa translocator protein (TSPO), which is upregulated in activated microglia and has previously been a PET target in Alzheimer’s disease research. Currently, the gold‐standard method for quantifying ER176 PET is kinetic modeling of the dynamic PET data using the arterial input function (AIF), which is invasive and expensive. The current study explores a pseudo‐reference region (RR) technique to noninvasively quantify ER176 scans to facilitate clinical PET studies.MethodDynamic 90‐min [11C]ER176 PET scans were acquired in 11 participants, 5 controls (5M/0F) and 6 patients (3 MCI‐3M/0F; 3 AD‐2M/1F). Arterial blood data were collected throughout the duration of the scan and were used along with the dynamic imaging data to determine regional total volume of distribution (VT) using the two‐tissue compartment model. Standardized Uptake Value Ratio (SUVR) was calculated by normalizing the radioactivity in each region by that of the reference region (cerebellar gray matter). We explored differences across methods (AIF‐derived VT vs. RR‐derived SUVR) and across groups (controls vs. patients).ResultWithin each group, the outcome measures from AIF and RR methods were well correlated (Controls: R2 = 0.986, p < 0.001; Patients: R2 = 0.983, p < 0.001; Figure 1). Both methods showed greater TSPO expression in patients compared to controls, with mean differences in the medial temporal cortex of 14.99% between groups for VT and 10.65% between groups for SUVR, but lower variance for SUVR compared to VT (Figure 2). Both quantification methods showed similar regional patterns of percent differences between groups, corresponding to tau progression along Braak staging (Figure 3).ConclusionResults suggest that using the cerebellum, which does not accumulate amyloid plaques or tau aggregates until end‐stage disease, as a reference region for [11C]ER176 may be a valid method for quantifying differences between AD patients and controls compared to gold‐standard methodology. Regional increases in neuroinflammation are more reliable for SUVR than VT in AD‐related regions. Ongoing work includes a larger sample size to further validate noninvasive PET quantification.

Organization of pRF size along the AP axis of the hippocampus and adjacent medial temporal cortex is related to specialization for scenes versus faces.

The hippocampus is largely recognized for its integral contributions to memory processing. By contrast, its role in perceptual processing remains less clear. Hippocampal properties vary along the anterior-posterior (AP) axis. Based on past research suggesting a gradient in the scale of features processed along the AP extent of the hippocampus, the representations have been proposed to vary as a function of granularity along this axis. One way to quantify such granularity is with population receptive field (pRF) size measured during visual processing, which has so far received little attention. In this study, we compare the pRF sizes within the hippocampus to its activation for images of scenes versus faces. We also measure these functional properties in surrounding medial temporal lobe (MTL) structures. Consistent with past research, we find pRFs to be larger in the anterior than in the posterior hippocampus. Critically, our analysis of surrounding MTL regions, the perirhinal cortex, entorhinal cortex, and parahippocampal cortex shows a similar correlation between scene sensitivity and larger pRF size. These findings provide conclusive evidence for a tight relationship between the pRF size and the sensitivity to image content in the hippocampus and adjacent medial temporal cortex.

Gray matter and episodic memory associations with olfaction in middle-aged to older adults.

Age-related declines in olfaction contribute to low quality of life and appear to occur with declines in cognitive function, including diminished episodic memory. We tested the hypothesis that low gray matter volume within cortical regions that support olfaction and episodic memory can explain age-related differences in olfactory and episodic memory functions. T1-weighted images, Sniffin' Sticks olfactory measures, and the NIH Toolbox-Cognition Battery were administered to 131 middle-aged to older adults (50-86 years; 66% female). Correlation was used to examine the associations between these measures. A network-based image processing approach was then used to examine the degree to which spatial patterns of gray matter variance were related to the olfactory and cognitive measures. Structural equation modeling was used to characterize the relative specificity of olfactory, cognitive, gray matter, and aging associations. Olfactory threshold, discrimination, and identification exhibited small to medium effect size associations with episodic memory performance (rs=0.27-0.42, ps<0.002). Gray matter volume within medial temporal and orbitofrontal cortex was also related to olfactory (discrimination and identification) and episodic memory function (rs=0.21-0.36, ps<0.019). Age and episodic memory explained the same variance in olfaction that was explained by the medial temporal and orbitofrontal pattern of gray matter volume. The results of this cross-sectional study suggest that identifying mechanisms contributing to differences in medial temporal and orbitofrontal cortex will advance our understanding of co-morbid olfactory and cognitive declines.

Altered neural intrinsic oscillations in patients with multiple sclerosis: effects of cortical thickness.

To investigate the effects of cortical thickness on the identification accuracy of fractional amplitude of low-frequency fluctuation (fALFF) in patients with multiple sclerosis (MS). Resting-state functional magnetic resonance imaging data were collected from 31 remitting MS, 20 acute MS, and 42 healthy controls (HCs). After preprocessing, we first calculated two-dimensional fALFF (2d-fALFF) maps using the DPABISurf toolkit, and 2d-fALFF per unit thickness was obtained by dividing 2d-fALFF by cortical thickness. Then, between-group comparison, clinical correlation, and classification analyses were performed in 2d-fALFF and 2d-fALFF per unit thickness maps. Finally, we also examined whether the effect of cortical thickness on 2d-fALFF maps was affected by the subfrequency band. In contrast with 2d-fALFF, more changed regions in 2d-fALFF per unit thickness maps were detected in MS patients, such as increased region of the right inferior frontal cortex and faded regions of the right paracentral lobule, middle cingulate cortex, and right medial temporal cortex. There was a significant positive correlation between the disease duration and the 2d-fALFF values in the left early visual cortex in remitting MS patients (r = 0.517, Bonferroni-corrected, p = 0.008 × 4 < 0.05). In contrast with 2d-fALFF, we detected a positive correlation between the 2d-fALFF per unit thickness of the right ventral stream visual cortex and the modified Fatigue Impact Scale (MFIS) scores (r = 0.555, Bonferroni-corrected, p = 0.017 × 4 > 0.05). For detecting MS patients, 2d-fALFF and 2d- fALFF per unit thickness both performed remarkably well in support vector machine (SVM) analysis, especially in the remitting phase (AUC = 86, 83%). Compared with 2d-fALFF, the SVM model of 2d-fALFF per unit thickness had significantly higher classification performance in distinguishing between remitting and acute MS. More changed regions and more clinically relevant 2d-fALFF per unit thickness maps in the subfrequency band were also detected in MS patients. By dividing the functional value by the cortical thickness, the identification accuracy of fALFF in MS patients was detected to be potentially influenced by cortical thickness. Additionally, 2d-fALFF per unit thickness is a potential diagnostic marker that can be utilized to distinguish between acute and remitting MS patients. Notably, we observed similar variations in the subfrequency band.

Characterizing affiliative touch in humans and its role in advancing haptic design.

An emerging view in cognitive neuroscience holds that the extraction of emotional relevance from sensory experience extends beyond the centralized appraisal of sensation in associative brain regions, including frontal and medial-temporal cortices. This view holds that sensory information can be emotionally valenced from the point of contact with the world. This view is supported by recent research characterizing the human affiliative touch system, which carries signals of soft, stroking touch to the central nervous system and is mediated by dedicated C-tactile afferent receptors. This basic scientific research on the human affiliative touch system is informed by, and informs, technology design for communicating and regulating emotion through touch. Here, we review recent research on the basic biology and cognitive neuroscience of affiliative touch, its regulatory effects across the lifespan, and the factors that modulate it. We further review recent work on the design of haptic technologies, devices that stimulate the affiliative touch system, such as wearable technologies that apply the sensation of soft stroking or other skin-to-skin contact, to promote physiological regulation. We then point to future directions in interdisciplinary research aimed at both furthering scientific understanding and application of haptic technology for health and wellbeing.

Longitudinal mapping of the development of cortical thickness and surface area in rhesus macaques during the first three years

Studying dynamic spatiotemporal patterns of early brain development in macaque monkeys is critical for understanding the cortical organization and evolution in humans, given the phylogenetic closeness between humans and macaques. However, due to huge challenges in the analysis of early brain Magnetic Resonance Imaging (MRI) data typically with extremely low contrast and dynamic imaging appearances, our knowledge of the early macaque cortical development remains scarce. To fill this critical gap, this paper characterizes the early developmental patterns of cortical thickness and surface area in rhesus macaques by leveraging advanced computing tools tailored for early developing brains based on a densely sampled longitudinal dataset with 140 rhesus macaque MRI scans seamlessly covering from birth to 36 mo of age. The average cortical thickness exhibits an inverted U-shaped trajectory with peak thickness at around 4.3 mo of age, which is remarkably in line with the age of peak thickness at 14 mo in humans, considering the around 3:1 age ratio of human to macaque. The total cortical surface area in macaques increases monotonically but with relatively lower expansions than in humans. The spatial distributions of thicker and thinner regions are quite consistent during development, with gyri having a thicker cortex than sulci. By 4 mo of age, over 81% of cortical vertices have reached their peaks in thickness, except for the insula and medial temporal cortices, while most cortical vertices keep expanding in surface area, except for the occipital cortex. These findings provide important insights into early brain development and evolution in primates.

Cognitive tasks affect the relationship between representational pattern similarity and subsequent item memory in the hippocampus

Episodic memories are records of personally experienced events, coded neurally via the hippocampus and surrounding medial temporal lobe cortex. Information about the neural signal corresponding to a memory representation can be measured in fMRI data when the pattern across voxels is examined. Prior studies have found that similarity in the voxel patterns across repetition of a to-be-remembered stimulus predicts later memory retrieval, but the results are inconsistent across studies. The current study investigates the possibility that cognitive goals (defined here via the task instructions given to participants) during encoding affect the voxel pattern that will later support memory retrieval, and therefore that neural representations cannot be interpreted based on the stimulus alone. The behavioral results showed that exposure to variable cognitive tasks across repetition of events benefited subsequent memory retrieval. Voxel patterns in the hippocampus indicated a significant interaction between cognitive tasks (variable vs. consistent) and memory (remembered vs. forgotten) such that reduced voxel pattern similarity for repeated events with variable cognitive tasks, but not consistent cognitive tasks, supported later memory success. There was no significant interaction in neural pattern similarity between cognitive tasks and memory success in medial temporal cortices or lateral occipital cortex. Instead, higher similarity in voxel patterns in right medial temporal cortices was associated with later memory retrieval, regardless of cognitive task. In conclusion, we found that the relationship between pattern similarity across repeated encoding and memory success in the hippocampus (but not medial temporal lobe cortex) changes when the cognitive task during encoding does or does not vary across repetitions of the event.

Preclinical Characterization of the Tau PET Tracer [18F]SNFT-1: Comparison of Tau PET Tracers.

Tau PET tracers are expected to be sufficiently sensitive to track the progression of age-related tau pathology in the medial temporal cortex. The tau PET tracer N-(4-[18F]fluoro-5-methylpyridin-2-yl)-7-aminoimidazo[1,2-a]pyridine ([18F]SNFT-1) has been successfully developed by optimizing imidazo[1,2-a]pyridine derivatives. We characterized the binding properties of [18F]SNFT-1 using a head-to-head comparison with other reported 18F-labeled tau tracers. Methods: The binding affinity of SNFT-1 to tau, amyloid, and monoamine oxidase A and B was compared with that of the second-generation tau tracers MK-6240, PM-PBB3, PI-2620, RO6958948, JNJ-64326067, and flortaucipir. In vitro binding properties of 18F-labeled tau tracers were evaluated through the autoradiography of frozen human brain tissues from patients with diverse neurodegenerative disease spectra. Pharmacokinetics, metabolism, and radiation dosimetry were assessed in normal mice after intravenous administration of [18F]SNFT-1. Results: In vitro binding assays demonstrated that [18F]SNFT-1 possesses high selectivity and high affinity for tau aggregates in Alzheimer disease (AD) brains. Autoradiographic analysis of tau deposits in medial temporal brain sections from patients with AD showed a higher signal-to-background ratio for [18F]SNFT-1 than for the other tau PET tracers and no significant binding with non-AD tau, α-synuclein, transactiviation response DNA-binding protein-43, and transmembrane protein 106B aggregates in human brain sections. Furthermore, [18F]SNFT-1 did not bind significantly to various receptors, ion channels, or transporters. [18F]SNFT-1 showed a high initial brain uptake and rapid washout from the brains of normal mice without radiolabeled metabolites. Conclusion: These preclinical data suggest that [18F]SNFT-1 is a promising and selective tau radiotracer candidate that allows the quantitative monitoring of age-related accumulation of tau aggregates in the human brain.

Correlation analysis between 18F-fluorodeoxyglucose positron emission tomography and cognitive function in first diagnosed Parkinson's disease patients.

Evaluation of the correlation between 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG PET) and cognitive function in first-diagnosed and untreated Parkinson's disease (PD) patients. This cross-sectional study included 84 first diagnosed and untreated PD patients. The individuals were diagnosed by movement disorder experts based on the 2015 MDS Parkinson's disease diagnostic criteria. The patients also underwent 18F-FDG PET scans and clinical feature assessments including the Montreal Cognitive Assessment (MoCA) scale. Glucose metabolism rates were measured in 26 brain regions using region of interest (ROI) and pixel-wise analyses with displayed Z scores. The cognitive function was assessed by professionals using the MoCA scale, which covers five cognitive domains. Spearman's linear correlation and linear regression models were used to compare the correlations between 18F-FDG metabolism in each brain region and cognitive domain, using SPSS 25.0 software. The results indicated a positive correlation between executive function and glucose metabolism in the lateral prefrontal cortex of the left hemisphere (p = 0.041). Additionally, a positive correlation between memory function and glucose metabolism in the right precuneus (p = 0.014), right lateral occipital cortex (p = 0.017), left lateral occipital cortex (p = 0.031), left primary visual cortex (p = 0.008), and right medial temporal cortex (p = 0.046). Further regression analysis showed that for every one-point decrease in the memory score, the glucose metabolism in the right precuneus would decrease by 0.3 (B = 0.30, p = 0.005), the glucose metabolism in the left primary visual cortex would decrease by 0.25 (B = 0.25, p = 0.040), the glucose metabolism in the right lateral occipital cortex would decrease by 0.38 (B = 0.38, p = 0.012), and the glucose metabolism in the left lateral occipital cortex would decrease by 0.32 (B = 0.32, p = 0.045). This study indicated that cognitive impairment in PD patients mainly manifests as changes in executive function, visual-spatial function and memory functions, while glucose metabolism mainly decreases in the frontal and posterior cortex. Further analysis shows that executive function is related to glucose metabolism in the left lateral prefrontal cortex. On the other hand, memory ability involves changes in glucose metabolism in a more extensive brain region. This suggests that cognitive function assessment can indirectly reflect the level of glucose metabolism in the relevant brain regions.

Comparison of quantitative FDG-PET and MRI in anti-LGI1 autoimmune encephalitis

ObjectivesAnti-leucine glioma-inactivated protein 1 (anti-LGI1) autoimmune encephalitis (AE) presents as subacute memory loss, behavioral changes, and seizures. Diagnosis and treatment delays can result in long term sequelae, including cognitive impairment. 18F-FDG PET/CT may be more sensitive than MRI in patients with AE. Our objective was to determine if anti-LGI1 is associated with a distinct pattern of FDG uptake and whether this pattern persists following treatment.MethodsNineteen18F-FDG PET/CT brain scans (13 pre-treatment, 6 convalescent phase) for 13 patients with anti-LGI1 were studied using NeuroQ™ and CortexID™. The sensitivity of the PET images was compared to MRI. The Z scores of 47 brain regions between the pre-treatment and next available follow-up images during convalescence were compared.ResultsAll 18F-FDG PET/CT scans demonstrated abnormal FDG uptake, while only 6 (42.9%) pre-treatment brain MRIs were abnormal. The pre-treatment scans demonstrated hypermetabolism in the bilateral medial temporal cortices, basal ganglia, brain stem, and cerebellum and hypometabolism in bilateral medial and mid frontal, cingulate, and parietotemporal cortices. Overall, the brain uptake during convalescence showed improvement of the Z scores towards 0 or normalization of previous hypometabolic activity in medial frontal cortex, inferior frontal cortex, Broca’s region, parietotemporal cortex, and posterior cingulate cortex and previous hypermetabolic activity in medial temporal cortices, caudate, midbrain, pons and cerebellum.ConclusionsBrain FDG uptake was more commonly abnormal than MRI in the pre-treatment phase of anti-LGI1, and patterns of dysmetabolism differed in the pre-treatment and convalescent phases. These findings may expedite the diagnosis, treatment, and monitoring of anti-LGI1 patients.

Lesion‐network mapping for post‐stroke cognitive impairment

AbstractBackgroundTo overcome difficulty obtaining functional neuroimaging data directly from acute stroke patients, researchers have developed a new mapping approach called the “indirect lesion‐network mapping.” This mapping approach uses a priori network information obtained from normative samples to account for brain areas functionally connected to patients’ local lesions. This new method has successfully mapped some peculiar stroke symptoms, such as aphasia and post‐stroke pain. However, it remains unclear whether this method can produce sensible mapping results for the post‐stroke cognitive impairment of community samples.MethodA total of 1,431 patients were enrolled from two university hospitals. They underwent a comprehensive neuropsychological test within one year post‐stroke. We used the Verbal Learning Test to examine memory performance and the trail‐making test‐A and digit symbol coding task to assess the speed of processing. We classified the patients into two groups— cognitively‐impaired (CI) (&lt; ‐2.0 standard deviation) and cognitively‐unimpaired (CU) (&gt; ‐1.0 SD)—based on the task performance. We calculated the lesion‐affected networks based on patients’ stroke lesions combined with the normative functional network data from the Human Connectome Project‐Aging cohort, n = 687. Then, we obtained and compared the lesion network maps for patients with versus without cognitive impairment.ResultA total of 348 patients showed impairment in the memory task, while 247 patients demonstrated impairment in the speed‐of‐processing task. For both tasks, the conventional lesion‐network mapping approach produced highly similar results between the CI versus CU groups—both groups were localized to the major hubs of the large‐scale functional networks, such as mid‐cingulate and insular cortices. However, the mapping results became more sensible when we contrasted the lesion‐network maps between the CI versus CU groups. For example, the memory impairment was localized to the functional network including the left medial and anterior temporal, inferior and dorsolateral frontal cortices, whereas the speed‐of‐processing impairment was localized to the network including both occipital and anterior temporal cortices.ConclusionWe suggest that some additional analysis steps to the conventional lesion‐network mapping, including the contrast analysis, can help improve the specificity and sensitivity of the results when targeting the stroke‐induced cognitive impairment of community samples.

Transcriptomic and glycomic analyses highlight pathway-specific glycosylation alterations unique to Alzheimer’s disease

Glycosylation has been found to be altered in the brains of individuals with Alzheimer’s disease (AD). However, it is unknown which specific glycosylation-related pathways are altered in AD dementia. Using publicly available RNA-seq datasets covering seven brain regions and including 1724 samples, we identified glycosylation-related genes ubiquitously changed in individuals with AD. Several differentially expressed glycosyltransferases found by RNA-seq were confirmed by qPCR in a different set of human medial temporal cortex (MTC) samples (n = 20 AD vs. 20 controls). N-glycan-related changes predicted by expression changes in these glycosyltransferases were confirmed by mass spectrometry (MS)-based N-glycan analysis in the MTC (n = 9 AD vs. 6 controls). About 80% of glycosylation-related genes were differentially expressed in at least one brain region of AD participants (adjusted p-values < 0.05). Upregulation of MGAT1 and B4GALT1 involved in complex N-linked glycan formation and galactosylation, respectively, were reflected by increased concentrations of corresponding N-glycans. Isozyme-specific changes were observed in expression of the polypeptide N-acetylgalactosaminyltransferase (GALNT) family and the alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase (ST6GALNAC) family of enzymes. Several glycolipid-specific genes (UGT8, PIGM) were upregulated. The critical transcription factors regulating the expression of N-glycosylation and elongation genes were predicted and found to include STAT1 and HSF5. The miRNA predicted to be involved in regulating N-glycosylation and elongation glycosyltransferases were has-miR-1-3p and has-miR-16-5p, respectively. Our findings provide an overview of glycosylation pathways affected by AD and potential regulators of glycosyltransferase expression that deserve further validation and suggest that glycosylation changes occurring in the brains of AD dementia individuals are highly pathway-specific and unique to AD.

APOEε4 associates with microglial activation independently of Aβ plaques and tau tangles.

Animal studies suggest that the apolipoprotein E ε4 (APOEε4) allele is a culprit of early microglial activation in Alzheimer's disease (AD). Here, we tested the association between APOEε4 status and microglial activation in living individuals across the aging and AD spectrum. We studied 118 individuals with positron emission tomography for amyloid-β (Aβ; [18F]AZD4694), tau ([18F]MK6240), and microglial activation ([11C]PBR28). We found that APOEε4 carriers presented increased microglial activation relative to noncarriers in early Braak stage regions within the medial temporal cortex accounting for Aβ and tau deposition. Furthermore, microglial activation mediated the Aβ-independent effects of APOEε4 on tau accumulation, which was further associated with neurodegeneration and clinical impairment. The physiological distribution of APOE mRNA expression predicted the patterns of APOEε4-related microglial activation in our population, suggesting that APOE gene expression may regulate the local vulnerability to neuroinflammation. Our results support that the APOEε4 genotype exerts Aβ-independent effects on AD pathogenesis by activating microglia in brain regions associated with early tau deposition.

Atrophy, hypometabolism and implication regarding pathology in late-life major depression with suspected non-alzheimer pathophysiology (SNAP)

BackgroundA substantial proportion of individuals with late-life major depression could be classified as having a suspected non-Alzheimer disease pathophysiology (SNAP), as indicated by a negative test for the biomarker β-amyloid (Aβ-) but a positive test for neurodegeneration (ND+). This study investigated the clinical features, characteristic patterns of brain atrophy and hypometabolism, and implications regarding pathology in this population. MethodsForty-six amyloid-negative patients with late-life major depressive disorder (MDD) patients, including 23 SNAP (Aβ-/ND+) and 23 Aβ-/ND- MDD subjects, and 22 Aβ-/ND-healthy control subjects were included in this study. Voxel-wise group comparisons between the SNAP MDD, Aβ-/ND- MDD and control subjects were performed, adjusting for age, gender and level of education. For exploratory comparisons, 8 Aβ+/ND- and 4 Aβ+/ND + MDD patients were included in the Supplementary Material. ResultsThe SNAP MDD patients had atrophy extending to regions outside the hippocampus, predominately in the medial temporal, dorsomedial and ventromedial prefrontal cortex; hypometabolism involving a large portion of the lateral and medial prefrontal cortex in addition to the bilateral temporal, parietal and precuneus cortex within typical Alzheimer disease regions were observed. Metabolism ratios of the inferior to the medial temporal lobe were significantly elevated in the SNAP MDD patients. We further discussed the implications with regards to underlying pathologies. ConclusionThe present study demonstrated characteristic patterns of atrophy and hypometabolism in patients with late-life major depression with SNAP. Identifying individuals with SNAP MDD may provide insights into currently unspecified neurodegenerative processes. Future refinement of neurodegeneration biomarkers is essential in order to identify potential pathological correlates while in vivo reliable pathological biomarkers are not forthcoming.