Neural mechanisms underlying probabilistic category learning in normal aging.

نویسندگان

  • Francesco Fera
  • Thomas W Weickert
  • Terry E Goldberg
  • Alessandro Tessitore
  • Ahmad Hariri
  • Sumitra Das
  • Sam Lee
  • Brad Zoltick
  • Martijn Meeter
  • Catherine E Myers
  • Mark A Gluck
  • Daniel R Weinberger
  • Venkata S Mattay
چکیده

Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been examined previously in probabilistic category learning. In the present study, using a blood oxygenation level-dependent functional magnetic resonance imaging block design, 18 healthy young adults (mean age, 25.5 +/- 2.6 years) and 15 older adults (mean age, 67.1 +/- 5.3 years) were assessed on the probabilistic category learning "weather prediction" test. Whole-brain functional images acquired using a 1.5T scanner (General Electric, Milwaukee, WI) with gradient echo, echo planar imaging (3/1 mm; repetition time, 3000 ms; echo time, 50 ms) were analyzed using second-level random-effects procedures [SPM99 (Statistical Parametric Mapping)]. Young and older adults displayed equivalent probabilistic category learning curves, used similar strategies, and activated analogous neural networks, including the prefrontal and parietal cortices and the caudate nucleus. However, the extent of caudate and prefrontal activation was less and parietal activation was greater in older participants. The percentage correct and reaction time were mainly positively correlated with caudate and prefrontal activation in young individuals but positively correlated with prefrontal and parietal cortices in older individuals. Differential activation within a circumscribed neural network in the context of equivalent learning suggests that some brain regions, such as the parietal cortices, may provide a compensatory mechanism for healthy older adults in the context of deficient prefrontal cortex and caudate nuclei responses.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Neural correlates of probabilistic category learning in patients with schizophrenia.

Functional neuroimaging studies of probabilistic category learning in healthy adults report activation of cortical-striatal circuitry. Based on previous findings of normal learning rate concurrent with an overall performance deficit in patients with schizophrenia, we hypothesized that relative to healthy adults, patients with schizophrenia would display preserved caudate nucleus and abnormal pr...

متن کامل

Learning Shapes the Representation of Visual Categories in the Aging Human Brain

The ability to make categorical decisions and interpret sensory experiences is critical for survival and interactions across the lifespan. However, little is known about the human brain mechanisms that mediate the learning and representation of visual categories in aging. Here we combine behavioral measurements and fMRI measurements to investigate the neural processes that mediate flexible cate...

متن کامل

A neuroplausible computational model of vision also exhibits asymmetry in developmental category learning

Computational models are increasingly used to explore possible mechanisms underlying infant capability in various tasks. Often, such models do not work directly on perceptual data, but on hand-computed features of images; such models are open to the criticism that these high-level features may not be what is actually computed in the neural computation. Here we explore the feasibility of the Ser...

متن کامل

Basal ganglia and dopamine contributions to probabilistic category learning.

Studies of the medial temporal lobe and basal ganglia memory systems have recently been extended towards understanding the neural systems contributing to category learning. The basal ganglia, in particular, have been linked to probabilistic category learning in humans. A separate parallel literature in systems neuroscience has emerged, indicating a role for the basal ganglia and related dopamin...

متن کامل

Novel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection

In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • The Journal of neuroscience : the official journal of the Society for Neuroscience

دوره 25 49  شماره 

صفحات  -

تاریخ انتشار 2005