Object category learning is a fundamental ability, requiring the combination of "bottom-up" stimulus-driven with "top-down" task-specific information. It therefore may be a fruitful domain for study of the general neural mechanisms underlying cortical plasticity. A simple model predicts that category learning involves the formation of a task-independent shape-selective representation that provides input to circuits learning the categorization task, with the computationally appealing prediction of facilitated learning of additional, novel tasks over the same stimuli. Using fMRI rapid-adaptation techniques, we find that categorization training (on morphed "cars") induced a significant release from adaptation for small shape changes in lateral occipital cortex irrespective of category membership, compatible with the sharpening of a representation coding for physical appearance. In contrast, an area in lateral prefrontal cortex, selectively activated during categorization, showed sensitivity posttraining to explicit changes in category membership. Further supporting the model, categorization training also improved discrimination performance on the trained stimuli.

Summary Theories of reading have posited the existence of a neural representation coding for whole real words (i.e. an orthographic lexicon), but experimental support for such a representation has proved elusive. Using fMRI rapid adaptation techniques, we provide evidence that the human left ventral occipitotemporal cortex (specifically the “visual word form area”, VWFA) contains a representation based on neurons highly selective for individual real words, in contrast to current theories that posit a sublexical representation in the VWFA.

Understanding the neural mechanisms underlying object recognition is one of the fundamental challenges of visual neuroscience. While neurophysiology experiments have provided evidence for a "simple-to-complex" processing model based on a hierarchy of increasingly complex image features, behavioral and fMRI studies of face processing have been interpreted as incompatible with this account. We present a neurophysiologically plausible, feature-based model that quantitatively accounts for face discrimination characteristics, including face inversion and "configural" effects. The model predicts that face discrimination is based on a sparse representation of units selective for face shapes, without the need to postulate additional, "face-specific" mechanisms. We derive and test predictions that quantitatively link model FFA face neuron tuning, neural adaptation measured in an fMRI rapid adaptation paradigm, and face discrimination performance. The experimental data are in excellent agreement with the model prediction that discrimination performance should asymptote as faces become dissimilar enough to activate different neuronal populations.

The nature of orthographic representations in the human brain is still subject of much debate. Recent reports have claimed that the visual word form area (VWFA) in left occipitotemporal cortex contains an orthographic lexicon based on neuronal representations highly selective for individual written real words (RWs). This theory predicts that learning novel words should selectively increase neural specificity for these words in the VWFA. We trained subjects to recognize novel pseudowords (PWs) and used fMRI rapid adaptation to compare neural selectivity with RWs, untrained PWs (UTPWs), and trained PWs (TPWs). Before training, PWs elicited broadly tuned responses, whereas responses to RWs indicated tight tuning. After training, TPW responses resembled those of RWs, whereas UTPWs continued to show broad tuning. This change in selectivity was specific to the VWFA. Therefore, word learning appears to selectively increase neuronal specificity for the new words in the VWFA, thereby adding these words to the brain's visual dictionary.

Objective Preclinical evidence with nilotinib, a US Food and Drug Administration (FDA)‐approved drug for leukemia, indicates improvement in Alzheimer's disease phenotypes. We investigated whether nilotinib is safe, and detectable in cerebrospinal fluid, and alters biomarkers and clinical decline in Alzheimer's disease. Methods This single‐center, phase 2, randomized, double‐blind, placebo‐controlled study investigated the safety, tolerability, and pharmacokinetics of nilotinib, and measured biomarkers in participants with mild to moderate dementia due to Alzheimer's disease. The diagnosis was supported by cerebrospinal fluid or amyloid positron emission tomography biomarkers. Nilotinib 150 mg versus matching placebo was taken orally once daily for 26 weeks followed by nilotinib 300 mg versus placebo for another 26 weeks. Results Of the 37 individuals enrolled, 27 were women and the mean (SD) age was 70.7 (6.48) years. Nilotinib was well‐tolerated, although more adverse events, particularly mood swings, were noted with the 300 mg dose. In the nilotinib group, central nervous system (CNS) amyloid burden was significantly reduced in the frontal lobe compared to the placebo group. Cerebrospinal fluid Aβ40 was reduced at 6 months and Aβ42 was reduced at 12 months in the nilotinib group compared to the placebo. Hippocampal volume loss was attenuated (−27%) at 12 months and phospho‐tau‐181 was reduced at 6 months and 12 months in the nilotinib group. Interpretation Nilotinib is safe and achieves pharmacologically relevant cerebrospinal fluid concentrations. Biomarkers of disease were altered in response to nilotinib treatment. These data support a larger, longer, multicenter study to determine the safety and efficacy of nilotinib in Alzheimer's disease. ANN NEUROL 2020 ANN NEUROL 2020;88:183–194