ATN incorporating cerebrospinal fluid neurofilament light chain detects frontotemporal lobar degeneration.

INTRODUCTION: The ATN framework provides an in vivo diagnosis of Alzheimer's disease (AD) using cerebrospinal fluid (CSF) biomarkers of pathologic amyloid plaques (A), tangles (T), and neurodegeneration (N). ATN is rarely evaluated in pathologically confirmed patients and its poor sensitivity to suspected non-Alzheimer's pathophysiologies (SNAP), including frontotemporal lobar degeneration (FTLD), leads to misdiagnoses. We compared accuracy of ATN (ATNTAU ) using CSF total tau (t-tau) to a modified strategy (ATNNfL ) using CSF neurofilament light chain (NfL) in an autopsy cohort. METHODS: ATNTAU and ATNNfL were trained in an independent sample and validated in autopsy-confirmed AD (n = 67) and FTLD (n = 27). RESULTS: ATNNfL more accurately identified FTLD as SNAP (sensitivity = 0.93, specificity = 0.94) than ATNTAU (sensitivity = 0.44, specificity = 0.97), even in cases with co-occurring AD and FTLD. ATNNfL misclassified fewer AD and FTLD as "Normal" (2%) than ATNTAU (14%). DISCUSSION: ATNNfL is a promising diagnostic strategy that may accurately identify both AD and FTLD, even when pathologies co-occur.

Event-based modeling of T1-weighted MRI is related to pathology in frontotemporal lobar degeneration due to tau and TDP.

BACKGROUND: In previous studies of patients with frontotemporal lobar degeneration due to tau (FTLD-tau) and FTLD due to TDP (FTLD-TDP), cortical volumes derived from T1-weighted MRI have been used to identify a sequence of volume loss according to arbitrary volumetric criteria. Event-based modeling (EBM) is a probabilistic, generative machine learning model that determines the characteristic sequence of changes, or "events", occurring during disease progression. EBM also estimates an individual patient's disease "stage" by identifying which events have already occurred. In the present study, we use an EBM analysis to derive stages of regional anatomic atrophy in FTLD-tau and FTLD-TDP, and validated these stages against pathologic burden. METHODS: Sporadic autopsy-confirmed patients with FTLD-tau (N = 42) and FTLD-TDP (N = 21), and 167 healthy controls with available T1-weighted images were identified. A subset of patients had quantitative digital histopathology of cortex performed at autopsy (FTLD-tau = 30, FTLD-TDP = 17). MRI images were processed, producing regional measures of cortical volumes. K-means clustering was used to find cortical regions with similar amounts of GM volume changes (n = 5 clusters). EBM was used to determine the characteristic sequence of cortical atrophy of identified clusters in autopsy-confirmed FTLD-tau and FTLD-TDP, and estimate each patient's disease stage by cortical volume biomarkers. Linear regressions related pathologic burden to EBM-estimated disease stages. RESULTS: EBM for cortical volume biomarkers generated statistically robust characteristic sequences of cortical atrophy in each group of patients. Cortical volume-based EBM-estimated disease stage was associated with pathologic burden in FTLD-tau (R2 = 0.16, p = 0.017) and FTLD-TDP (R2 = 0.51, p = 0.0008). CONCLUSIONS: We provide evidence that EBM can identify sequences of pathologically-confirmed cortical atrophy in sporadic FTLD-tau and FTLD-TDP.

Phases of volume loss in patients with known frontotemporal lobar degeneration spectrum pathology.

Frontotemporal lobar degeneration (FTLD) includes clinically similar FTLD-tau or FTLD-TDP proteinopathies which lack in vivo markers for accurate antemortem diagnosis. To identify early distinguishing sites of cortical atrophy between groups, we retrospectively analyzed in vivo volumetric MRI from 42 FTLD-Tau and 21 FTLD-TDP patients and validated these findings with postmortem measures of pathological burden. Our frequency-based staging model revealed distinct loci of maximal early cortical atrophy in each group, including dorsolateral and medial frontal regions in FTLD-Tau and ventral frontal and anterior temporal regions in FTLD-TDP. Sørenson-Dice calculations between proteinopathy groups showed little overlap of phases. Conversely, within-group subtypes showed good overlap between 3R- and 4R-tauopathies, and between TDP-43 Types A and C for early regions with subtle divergence between subtypes in subsequent phases of atrophy. Postmortem validation found an association of imaging phases with pathologic burden within FTLD-tau (F(4, 238) = 17.44, p < 0.001) and FTLD-TDP (F(4,245) = 42.32, p < 0.001). These results suggest that relatively early, distinct markers of atrophy may distinguish FTLD proteinopathies during life.

A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England.

BACKGROUND: As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. METHODS: Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. RESULTS: Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots, including job descriptions and clinical tools. These can be customised for reuse by other services. CONCLUSIONS: Healthcare services need to translate advances in genetics into benefits for patients. Consideration of the issues presented here when incorporating genetics into mainstream medical services will help ensure that new service developments build on the body of experience gained by the pilots, to provide high quality services for patients with or at risk of genetic conditions.

Compensatory Neural Responses to Cognitive Fatigue in Young and Older Adults.

Prolonged performance of a demanding cognitive task induces cognitive fatigue. We examined the behavioral and neural responses to fatigue-induced cognitive impairments in young and older adults. Particular emphasis was placed on whether the brain exhibited compensatory neural activity in response to cognitive fatigue. High-density EEG was recorded from a young (n = 16; 18-33 years of age) and an older (n = 18; 60-87 years of age) cohort who performed a Stroop task continuously for ∼2 h with no breaks. In the young cohort, behavioral performance declined as the experiment progressed, reflecting the deleterious effects of cognitive fatigue. Neurophysiologically, in addition to declining neural activity as cognitive fatigue developed, there is also evidence of region- and time-specific increase in neural activity, suggesting neural compensation. The compensatory activities followed patterns paralleling that of posterior-anterior shift in aging (PASA) and early to late shift in aging (ELSA) observed in cognitive aging and helped to moderate fatigue-induced behavioral deterioration. In the older cohort, behavioral performance did not decline as the experiment progressed, and neural activity either declined or stayed unchanged, showing no evidence of neural compensation, in contrast to the young. These results suggest that young and older adults coped with cognitive fatigue differently by exhibiting differential responses as a function of time-on-task at both the behavioral level and the neural level.

Structural brain correlates of fatigue in older adults with and without Parkinson's disease.

Fatigue is one of the most common and disabling nonmotor symptoms seen in Parkinson's disease (PD) and is also commonly seen in healthy older adults. Our understanding of the etiology of fatigue in older adults with or without PD is limited and it remains unclear whether fatigue in PD is specifically related to PD pathology. The objective of this study was thus to determine whether fatigue in PD was associated with structural changes in gray or white matter and explore whether these changes were similar in older adults without PD. Magnetic resonance imaging (T1 weighted) and diffusion tensor imaging were performed in 60 patients with PD (17 females; age = 67.58 ±â€¯5.51; disease duration = 5.67 ±â€¯5.83 years) and 41 age- and sex- matched healthy controls. FSL image processing was used to measure gray matter volume, fractional anisotropy, and leukoariosis differences. Voxel-based morphometry confirmed gray matter loss across the dorsal striatum and insula in the PD patient cohort. PD patients with fatigue had reduced gray matter volume in dorsal striatum relative to PD patients without fatigue (P < 0.05 False Discovery Rate corrected). No significant fatigue-related structural atrophy was found in controls. There were no areas of significant fractional anisotropy differences between high and low fatigue subjects in either the PD or non-PD groups. Control participants with high fatigue, but not PD, showed significantly greater total leukoariosis volumes (p = 0.03). Fatigue in PD is associated with unique structural changes in the caudate and putamen suggesting fatigue in PD is primarily related to PD pathology, particularly in the dorsal striatum, and not simply a consequence of aging.

Task-Based Cognitive Fatigability for Older Adults and Validation of Mental Fatigability Subscore of Pittsburgh Fatigability Scale.

Cognitive fatigue and cognitive fatigability are distinct constructs. Cognitive fatigue reflects perception of cognitive fatigue outside of the context of activity level and duration and can be reliably assessed via established instruments such as the Fatigue Severity Scale (FSS) and the Modified Fatigue Impact Scale (MFIS). In contrast, cognitive fatigability reflects change in fatigue levels quantified within the context of the level and duration of cognitive activity, and currently there are no reliable measures of cognitive fatigability. A recently published scale, the Pittsburgh Fatigability Scale (PFS), attempts to remedy this problem with a focus on the aged population. While the physical fatigability subscore of PFS has been validated using physical activity derived measures, the mental fatigability subscore of PFS remains to be tested against equivalent measures derived from cognitive activities. To this end, we recruited 35 older, healthy adult participants (mean age 73.77 ± 5.9) to complete the PFS as well as a prolonged continuous performance of a Stroop task (>2 h). Task-based assessments included time-on-task changes in self-reported fatigue scores (every 20 min), reaction time, and pupil diameter. Defining subjective fatigability, behavioral fatigability, and physiologic/autonomic fatigability to be the slope of change over time-on-task in the above three assessed variables, we found that the PFS mental subscore was not correlated with any of the three task-based fatigability measures. Instead, the PFS mental subscore was correlated with trait level fatigue measures FSS (ρ = 0.63, p < 0.001), and MFIS cognitive subsection (ρ = 0.36, p = 0.03). This finding suggested that the PFS mental fatigability subscore may not be an adequate measure of how fatigued one becomes after a given amount of mental work. Further development efforts are needed to create a self-report scale that reliably captures cognitive fatigability in older adults.