In neurodegenerative diseases, specific brain regions take the brunt of pathology and atrophy. However, differences from one patient to another make it hard to predict the precise path of progression for any one person. Now, scientists led by Jesse Brown and Bill Seeley of the University of California, San Francisco, report that they used a single structural MRI from 72 patients with frontotemporal dementia to predict which areas of their brains would next succumb to the disease. They determined where the disease likely originated—its epicenter—and used common functional connectivity maps to extrapolate regions likely to atrophy. In practice, the predictions correlated with brain volume loss over the next several years. The method provides an individualized biomarker for early clinical trials, the authors suggest.
- Baseline MRI scans pinpoint disease epicenter for individual patients.
- Model predicts future atrophy among functionally connected regions.
- Prediction model could be biomarker for early stage clinical trials.
“Patient-tailored biomarkers represent a critical step forward for the field,” wrote Alexandre Bejanin, Research Institute of the Hospital de la Santa Creu i Sant Pau, Barcelona, Spain, to Alzforum. “They can provide core information on the clinical evolution of patients with neurodegenerative disorders, play a crucial role in monitoring disease progression in clinical trials, and guide future prevention programs and treatments.” Bejanin was not involved in the study.
“I find this work to be important and interesting in that the authors provide a novel framework for single-subject outcome measures based on network physiology,” wrote David Jones, Mayo Clinic, Rochester, Minnesota, to Alzforum. “This provides more evidence that the key biology at play in neurodegenerative diseases of the brain involves the functional physiology of large-scale brain networks that support mental functioning.” Jones was also uninvolved in the study.
A prominent theory for the progression of brain diseases argues that misfolded, pathological proteins pass from neuron to neuron via trans-synaptic spread (de Calignon et al., 2012; Frost and Diamond, 2010). Based on that idea, Seeley and colleagues previously proposed that disease starts in one region of the brain, the epicenter, and spreads to new regions that are functionally connected (Seeley, 2017).
Most of the evidence for this came from cross-sectional data. It remained to be proven if such a model predicted longitudinal atrophy in individual patients. In the current study, the authors sought to do just that in patients with either behavioral variant frontotemporal dementia (bvFTD) or the semantic variant of primary progressive aphasia (svPPA), two forms of frontotemporal dementia with distinct patterns of atrophy. They guessed that within a particular disorder, individual patients would have distinct epicenters that would dictate differences in overall disease progression.
Brown and colleagues examined structural MRI scans taken from 70 patients and 265 healthy controls. From the controls, the researchers derived a typical path of normal age-related volume loss. Then for each patient, the researchers calculated the difference between a region’s expected volume for that age and sex, and the actual measurement. That gave a map of a patient’s own disease-related atrophy pattern. To find its epicenter, they overlaid these patterns onto intrinsic connectivity maps, which show the functional connections between different regions in the normal human brain. The region’s intrinsic connectivity map that most closely resembled the constellation of atrophied regions in the patient’s brain was deemed the epicenter of disease.
In general, epicenters overlapped among patients with the same syndrome. In patients with bvFTD, they most often resided in the anterior cingulate cortex and frontoinsular cortex. In people with svPPA, the epicenter tended to be near the anterior temporal lobe. Even so, exact epicenter locations still varied considerably from person to person. Interestingly, while these epicenters always exhibited signs of atrophy, they were not necessarily the areas of greatest volume loss. Within each disease, atrophy patterns differed substantially between people.
To identify which regions would atrophy as disease progressed, the researchers built a prediction model that took into account three factors: how functionally connected a region was to the epicenter, how much its nearest neighbors had already been shrinking, and its baseline volume loss. From this, the researchers determined likely atrophy patterns for 42 bvFTD and 30 svPPA patient volunteers. Those participants then had an average of three more scans separated by six to 14 months, and the researchers compared the expected with the actual loss.
For most, those atrophy patterns matched up fairly well, with a correlation coefficient of 0.65. For 16 patients, 13 with bvFTD, the model failed to predict volume loss, with an average correlation coefficient of -0.04. Most of these patients had limited baseline atrophy and an unclear epicenter.
The researchers found that the rate of volume loss differed among brain regions. The most shrinkage occurred not in the epicenters themselves, but among their first-degree network neighbors. Possibly, the epicenter had already degenerated as much as it would, while first-degree neighbors were just getting started, the authors reasoned. “This has fundamental implications for clinical trials that would use imaging-derived indexes as an outcome,” wrote Bejanin. “The best regions to assess the effect of disease-modifying drugs should not be those primarily targeted by the disease, nor the most atrophied, but those most connected to these areas.”
The model isn’t refined enough to be used in clinical trials just yet, said Seeley. He noted that in this study, pooled functional connectome data of a group of healthy individuals were used to predict connectivity, but using a patient’s own connectome would likely improve predictions. But with better accuracy, this model could provide a proof-of-concept indicator for early to mid-stage clinical trials. For example, if a therapy leads to less atrophy than expected, it could encourage stakeholders to proceed with a confirmatory trial. Rik Ossenkoppele, Amsterdam University Medical Center, the Netherlands, added that the model could allow placebo and treatment groups to be better matched for expected atrophy rates. Perhaps a group of “fast progressors” could be identified and included to keep trials shorter and smaller, he suggested.
Seeley said that a more distant goal is to predict atrophy well enough to advise patients in the clinic about what symptoms to expect in the future. He expects that the model will be useful in other neurodegenerative diseases, including Alzheimer’s, although the double proteinopathy of Aβ and tau will likely render the picture more complicated.—Gwyneth Dickey Zakaib
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