Satoshi Minoshima

Limbic-predominant age-related transactive response DNA-binding protein 43 kDa encephalopathy (LATE) is emerging as a prevalent neurodegenerative disorder in aging populations, mimicking the clinical presentation of Alzheimer disease (AD). This study investigates in vivo [¹⁸F]FDG PET and MRI biomarkers in detecting probable LATE neuropathologic change. Methods: We retrospectively analyzed 944 [¹⁸F]FDG PET cases referred from cognitive disorder clinics in a tertiary care center. To characterize the LATE and AD findings objectively and quantitatively, we created 3-dimensional stereotactic surface projection PET templates for LATE neuropathologic change (n = 6) and AD neuropathologic change (n = 32) from autopsy-confirmed Alzheimer's Disease Neuroimaging Initiative and University of Utah datasets, respectively. All 3-dimensional stereotactic surface projection z score [¹⁸F]FDG maps were created in comparison to normal PET scans from 20 control cases whose amyloid PET scans were negative. Using the autopsy-derived z score maps, z score product indices (the individual z score map multiplied by the z scores of autopsy-confirmed cohorts) were generated for each subject, stratifying participants into probable LATE, probable LATE and AD (LATE+AD), and probable AD. Clinical and quantitative MRI volumetry data were compared across the groups using 1-way or Welch ANOVA and Fisher exact tests, depending on the assessed variables. Results: Of the 944 clinical cases, 13.0% were characterized as probable LATE (2.4% pure LATE and 10.6% LATE+AD) and 23.7% were characterized as probable AD without LATE. MRI volumetry revealed that the medial temporal lobe was most affected in pure LATE cases (P < 0.001), whereas the orbitofrontal gyrus and lateral temporal lobe were most vulnerable in mixed LATE+AD cases (P = 0.001; P < 0.001). Post hoc analysis identified the entorhinal cortex and amygdala as key regions for distinguishing mixed LATE+AD cases from pure LATE and pure AD cases, respectively (P = 0.05; P < 0.001). Subgroup analysis of the probable LATE+AD group demonstrated additive or synergistic effects of both pathologies, with three quarters of cases exhibiting concordant lateralized metabolic brain changes, predominantly left-sided, based on LATE and AD z score products (P < 0.001). A similar pattern of left-dominant brain atrophy was observed in MRI volumetry. Conclusion: Substantial numbers of our patients exhibited LATE features that were characterized objectively using scans from autopsy-proven cases. These LATE cases were associated with specific regional atrophy measured by quantitative MRI. Cases with LATE+AD copathologies demonstrated synergistic hemispheric involvement. Further investigations of such synergistic changes between LATE and AD are warranted.

Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently recognized neurodegenerative disorder that frequently affects elderly patients and is often misdiagnosed as Alzheimer’s disease. It typically presents with mild memory impairment and disproportionate hippocampal atrophy. FDG PET commonly shows distinct medial temporal lobe hypometabolism, which differs from Alzheimer’s disease. We report a case with FDG PET findings suggestive of LATE; however, CT and MRI revealed hippocampal sulcus remnant cysts instead of hippocampal atrophy. This highlights the critical importance of correlative imaging in distinguishing LATE from other conditions with similar PET findings.

Developmental venous anomaly (DVA) is a common benign brain vascular malformation, typically asymptomatic, with intervention required when complications arise. We present the case of a 63-year-old man with mild cognitive decline whose brain FDG PET showed decreased uptake not only at the site of DVA, but in the extended surrounding parenchyma where MRI showed no parenchymal abnormalities. DVAs arise from developmental irregularities in the embryonic venous system, but likely affecting a greater extent of the surrounding parenchymal functions. These findings highlight the clinical significance of DVAs and their effects on the parenchymal function seen on FDG PET albeit normal parenchyma on structural imaging.

A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. The majority of ¹⁸F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer’s disease amyloid pathology. Minimizing technical variance allows precise, quantitative, standardized uptake value ratios (SUVRs) for early detection of a𝛽 amyloid plaques and the effectiveness of anti-amyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance (QIBA) Amyloid PET Biomarker Committee developed and validated a Profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: Upon achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation (wCV) of ≤1.94% when the same radiopharmaceutical, scanner, acquisition and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers and potential future developments of the Profile to achieve precision in clinical and research amyloid PET imaging.

Since the invention of ¹⁸F-FDG as a neurochemical tracer in the 1970s, ¹⁸F-FDG PET has been used extensively for dementia research and clinical applications. FDG, a glucose analog, is transported into the brain via glucose transporters and metabolized in a concerted process involving astrocytes and neurons. Although the exact cellular mechanisms of glucose consumption are still under investigation, ¹⁸F-FDG PET can sensitively detect altered neuronal activity due to neurodegeneration. Various neurodegenerative disorders affect different areas of the brain, which can be depicted as altered ¹⁸F-FDG uptake by PET. The spatial patterns and severity of such changes can be reproducibly visualized by statistical mapping technology, which has become widely available in the clinic. The differentiation of 3 major neurodegenerative disorders by ¹⁸F-FDG PET, Alzheimer disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), has become standard practice. As the nosology of FTD evolves, frontotemporal lobar degeneration, the umbrella term for pathology affecting the frontal and temporal lobes, has been subclassified clinically into behavioral variant FTD; primary progressive aphasia with 3 subtypes, semantic, nonfluent, and logopenic variants; and movement disorders including progressive supranuclear palsy and corticobasal degeneration. Each of these subtypes is associated with differential ¹⁸F-FDG PET findings. The discovery of new pathologic markers and clinicopathologic correlations via larger autopsy series have led to newly recognized or redefined disease categories, such as limbic-predominant age-related TDP-43 encephalopathy, hippocampus sclerosis, primary age-related tauopathy, and argyrophilic grain disease, which have become a focus of investigations by molecular imaging. These findings need to be integrated into the modern interpretation of ¹⁸F-FDG PET. Recent pathologic investigations also have revealed a high prevalence, particularly in the elderly, of mixed dementia with overlapping and coexisting pathologies. The interpretation of ¹⁸F-FDG PET is evolving from a traditional dichotomous diagnosis of AD versus FTD (or DLB) to a determination of the most predominant underlying pathology that would best explain the patient’s symptoms, for the purpose of care guidance. ¹⁸F-FDG PET is a relatively low cost and widely available imaging modality that can help assess various neurodegenerative disorders in a single test and remains the workhorse in clinical dementia evaluation.

Initial development of artificial Intelligence (AI) and machine learning (ML) dates back to the mid-twentieth century. A growing awareness of the potential for AI, as well as increases in computational resources, research, and investment are rapidly advancing AI applications to medical imaging and, specifically, brain molecular imaging. AI/ML can improve imaging operations and decision making, and potentially perform tasks that are not readily possible by physicians, such as predicting disease prognosis, and identifying latent relationships from multi-modal clinical information. The number of applications of image-based AI algorithms, such as convolutional neural network (CNN), is increasing rapidly. The applications for brain molecular imaging (MI) include image denoising, PET and PET/MRI attenuation correction, image segmentation and lesion detection, parametric image formation, and the detection/diagnosis of Alzheimer’s disease and other brain disorders. When effectively used, AI will likely improve the quality of patient care, instead of replacing radiologists. A regulatory framework is being developed to facilitate AI adaptation for medical imaging.

Purpose: To demonstrate that magnetic resonance‐guided focused ultrasound (MRgFUS) facilitates blood‐spinal cord barrier (BSCB) permeability and develop observer‐independent MRI quantification of BSCB permeability after MRgFUS for spinal cord injury (SCI).Methods: Noninjured Sprague‐Dawley rats (n = 3) underwent MRgFUS and were administered Evans blue post‐MRgFUS to confirm BSCB opening. Absorbance was measured by spectrophotometry and correlated with its corresponding image intensity. Rats (n = 21) underwent T8–T10 laminectomy and extradural compression of the spinal cord (23g weighted aneurysm‐type clip, 1 min). The intervention group (n = 11) was placed on a preclinical MRgFUS system, administered microbubbles (Optison, 0.2 mL/kg), and received 3 MRgFUS sonications (25 ms bursts, 1 Hz pulses for 3 min, 3 acoustic W, approximately 1.0–2.1 MPa peak pressure as measured via hydrophone). The sham group (n = 10) received equivalent procedures with no sonications. T1w MRI was obtained both pre‐ and post‐MRgFUS BSCB opening. Spinal cords were segmented manually or semiautomatically and a Pearson correlation with P ≤ 0.001 was used to correlate the two segmentation methods. MRgFUS sonication and control regions intensity values were evaluated with a paired t‐test with a P ≤ 0.01.Results: Semiautomatic segmentation reduced computational time by 95% and was correlated with manual segmentation (Pearson = 0.92, P < 0.001, n = 71 regions). In the noninjured rat group, Evans blue absorbance correlated with image intensity in the MRgFUS and control regions (Pearson = 0.82, P = 0.02, n = 6). In rats that underwent the SCI procedure, an increase in signal intensity in the MRgFUS targeted region relative to control was seen in all SCI rats (10.65 ± 12.4%, range: 0.96–43.9%, n = 11, P = 0.002). SCI sham MRgFUS revealed no change (0.63 ± 0.52%, 95% CI 0.320.95, n = 10). This result was significant between both groups (P = 0.003).Conclusion: The implemented semiautomatic segmentation procedure improved data analysis efficiency. Quantitative methods using contrast‐enhanced MRI with histological validation are sensitive for detection of blood‐spinal cord barrier opening induced by magnetic resonance‐guided focused ultrasound.