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Open access peer-reviewed chapter

Changes in Visual Cortex in Healthy Aging and Dementia

Written By

Alyssa A. Brewer and Brian Barton

Reviewed: 09 June 2016 Published: 28 September 2016

DOI: 10.5772/64562

Update on Dementia IntechOpen
Update on Dementia Edited by Davide Vito Moretti

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Update on Dementia

Edited by Davide Vito Moretti

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Abstract

This chapter reviews the differences in specific structural and functional characteristics of human visual cortex among young adults, healthy aging adults, and patients with dementia, with a primary focus on those with Alzheimer’s disease (AD). Such visual cortex changes have been shown to underlie many of the behavioral deficits that develop in healthy aging and AD. Measurements of disordered visual cortex in dementia patients may be possible early in the course of neurodegeneration and thus may be useful for improving early diagnosis of these devastating diseases.

Keywords

  • visual cortex
  • visual field mapping
  • dementia
  • healthy aging
  • functional neuroimaging

1. Introduction

1.1. Overview of visual cortex characteristics

Human visual cortex can be partitioned into distinct topographical representations of visual space called visual field maps (VFMs), each of which subserves separate perceptual functions spanning the hierarchical stages of visual processing [1–3]. The organization of a VFM follows the organization of the retina; hence, retinotopic VFMs are cortical regions in which nearby neurons analyze the properties of nearby points of an image on the retina, and thus of visual space. This VFM organization is one of the more important, larger-scale, organizing principles of visual cortex. Such topographic organization is thought to allow for efficient connectivity among neurons that represent nearby aspects of visual space, likely necessary for such processes as lateral inhibition and to compactly organize neural signals ranging from the molecular level to that of the cerebral hemisphere [4–7]. In addition, measurements of the characteristics of these VFMs, together with an understanding of the stimulus selectivity of the neurons within them, is the foundation for understanding the specific visual computations carried out in particular cortical regions. Not only are such in vivo measurements of VFMs essential for the study of visual processing in healthy subjects, but they also are very effective for tracking changes in visual cortex in response to changes in visual inputs such as those that arise from retinal or cortical damage [8–12].

Although historically the organization and function of visual cortex have been primarily well characterized in healthy young adults or young patient populations [1–3, 8, 12–16], many behavioral studies [17–38] and more recent neuroimaging studies [36, 37, 39–55] are suggesting that several changes occur within visual pathways during what is considered otherwise healthy aging. These studies specifically describe retinal and cortical changes, rather than optical changes in the eye, that primarily contribute to the decreases in visual acuity and related issues that have been measured in healthy aging subjects [19, 33, 46, 50, 54]. These healthy aging changes are also now starting to be compared to pathophysiological changes in visual cortex in age-related disorders like dementia, including diseases such as Alzheimer’s disease (AD), posterior cortical atrophy (PCA), and dementia with Lewy bodies (DLB), in the hopes of both improving our understanding of these diseases and aiding improvements in potential therapies [37, 56].

Visual deficits are reported surprisingly often as one of the first symptoms of AD, the most common form of dementia. These deficits can include problems with visual-spatial attention, visual-spatial tasks, and visual-processing speed [37, 40, 47, 57–75]. A subset of dementia patients presents with prominent visual symptoms such as problems with visual field defects, contrast sensitivity, color discrimination, and feature recognition of complex objects, but little initial decline in memory. These patients have increased neuropathology in visual cortex, as compared to typical AD patients [76–84]. Dementia with these characteristics was initially often referred to as visual variant AD, but it is now usually termed PCA. DLB, the second most common type of dementia, also often presents with visual complaints [85]. Like in AD and PCA, the central features of DLB include progressive cognitive decline, typically with impairments in memory, visual-spatial abilities, and attention. However, unlike AD and PCA, one of the earliest visual symptoms in this disease is commonly visual hallucination [86].

Early detection and accurate diagnosis are keys in the hope for a cure for such dementias, as early, precise diagnosis would allow for more timely initiation of treatments. As visual symptoms can occur early in these diseases, studies are beginning to demonstrate that measurements of related changes in visual cortex in these patients could aid early detection of neurodegeneration. The highly structured representations within VFMs afford a fundamental measurement that might be used to detect subtle effects of neurodegeneration early in the disease process. Clear measures of the progression of the pathology within visual cortex might also help to target drug research for therapeutic interventions, especially by differentiating among different types of dementia [62]. The information reviewed in this chapter will serve as a foundation for subsequent use of this knowledge in our evaluation, interpretation, and treatment of these diseases. As little has been studied regarding VFMs in patients with PCA or DLB, this chapter will focus on measurements from patients with AD.

1.2. Review methodology

We performed a systematic review to investigate whether there are changes in early VFMs V1, V2, V3, and hV4 characteristics to healthy aging subjects and patients with AD or related dementias that can be measured using functional magnetic resonance imaging (fMRI). In order to include as many relevant citations as possible, we searched a comprehensive range of scientific databases including CogPrints, FreeFullPDF, Google Scholar, IngentaConnect, JSTOR, Mendeley, Microsoft Academic Search, PubMed, PubPsych, ResearchGate, and Web of Science. In addition, the Google search engine was used to find institutional, professional, and personal webpages hosting potentially relevant PDFs or citation links. Searches were performed from November 2015, to May 2016. Search queries included the following terms in various combinations: visual cortex, visual field map, visual area, primary visual cortex, V1, V2, V3, hV4, occipital cortex, visual changes, fMRI, pRF modeling/modeling, neuroimaging, aging, healthy aging, human, dementia, neurodegeneration, mild cognitive impairment, Alzheimer’s disease, dementia with Lewy bodies, Lewy body dementia, and posterior cortical atrophy. Related citation links available for the various database searches were routinely evaluated to investigate additional potential citations of interest. All potential study designs were included, and potentially relevant citations spanned the dates 1925–2016; note that visual field mapping with fMRI was relatively recently introduction in 1994 [87].

Ultimately, 317 potentially relevant citations were downloaded to EndNote for further evaluation. The full papers of these citations were assessed and selected to only include studies that directly related to comparing fMRI measures of visual cortex or visual field maps in healthy young adults (aged 18–40 years) to changes in visual cortex or visual field maps in healthy aging subjects with no known age-related diseases or specific, clinically recognized deficits in vision (aged 55–80 years) and/or to patients with mild-to-moderate AD with no known visual deficits unrelated to dementia (aged 55–80 years). No studies of VFM changes in DLB or PCA were found during the initial database searches, which was not unexpected given the very recent emergence of interest in visual field mapping in dementia. Additional studies of visual field mapping methodology and of visual symptoms associated with healthy aging, AD, DLB, and PCA were also retained for methods discussion and hypothesis development, respectively. Studies were further evaluated for quality and were excluded if they lacked statistical analysis or if they did not use accurate cortical field mapping techniques with individual subject analysis (for further discussion of these criteria, see [1, 3, 88]).

Of the 317 downloaded studies, 165 papers were directly relevant to this review, of which 88 focused on visual changes in aging or dementia. Of these, 24 studies measured changes specifically in visual cortex related to aging and/or dementia, and three studies explicitly investigated VFM changes in aging and/or dementia, as appropriate with respect to the inclusion and exclusion criteria [36, 37, 39]. Here, we provide both a narrative discussion of all 165 sources as well as graphical examples drawn from the three key visual field mapping studies. We discuss what is known regarding changes in early visual cortex during healthy aging and AD, how these findings relate to visual symptoms in these conditions, and what remains to be studied, and we recommend directions for future research.

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2. The measurement and organization of visual cortex in healthy young adults

In order to carefully evaluate alterations of visual cortex in healthy aging and age-related neurodegenerative diseases such as AD, we must first have an accurate and detailed understanding of the characteristics of visual cortex in healthy young adults. Visual cortex encompasses nearly 20% of the human cortical sheet, and studies are demonstrating that nearly all of it is organized into VFMs [1, 3]. Because many calculations are necessary to produce our visual world, our brains have many specialized VFMs which perform one or more of those calculations [3, 89]. Most—if not all—of these calculations are performed across the entire visual scene; color perception, for example, occurs throughout the visual field, not just in the lower right quadrant. Ultimately, it will be very interesting to investigate changes in visual cortex among all levels of the cortical visual hierarchy, from low-level visual processing in primary visual cortex (V1) to mid-level specialization like motion processing to higher-level processing like face recognition and visual-spatial attention [2, 90]. For now, studies of plasticity and neurodegeneration primarily investigate the lower level visual areas like V1, V2, V3, and hV4, as these VFMs are particularly well established in healthy young adults, relatively uncontroversial, and typically easily measured across most types of patients [1, 3, 8, 12, 14, 15, 37, 91].

Figure 1.

Schematic of measurements of an individual voxel. (A) Within a typical voxel measured with a standardly used 3T MRI scanner, there are on the order of ∼1 million neurons, depending on the size of the voxel. For voxels in retinotopic visual cortex, the neurons each have similarly located spatial receptive fields (teal circles with black outlines) with preferred centers (black dots). Note how the overlapping receptive fields concentrate coverage in one region of visual space (darker teal) corresponding to the average receptive field of the group, as shown in (D). (B) Each typical voxel is on the order of 1 × 1 × 1 mm for structural measurements and 1 × 1 × 3 mm for visual field mapping functional experiments, though voxels are often slightly larger (e.g., 3 × 3 × 3 mm) for other types of functional MR studies. (C) Traveling wave retinotopy (TWR) utilizes the organization of retinotopic cortex, in which neighboring neurons have preferred centers (black dot) representing similar portions of visual space, to estimate the average preferred representation (center) for the population of neurons in a given voxel (e.g., [1, 3]). (D) Population receptive field (pRF) modeling similarly utilizes retinotopic organization to estimate not only the preferred center (black dot) in a given voxel, but also the average receptive field—the population receptive field (teal circle)—for the voxel’s population of neurons (e.g., [1, 3, 92]).

2.1. Human visual field map measurements use simple stimuli and tasks, but provide exquisite detail of cortical organization

We briefly review here two of the most powerful fMRI techniques for very detailed measurements of VFMs in individual subjects, traveling wave retinotopy (TWR) [87] and population receptive field (pRF) modeling [92], to demonstrate what types of measurements are possible and how to interpret the existing literature regarding changes in aging or damaged visual cortex (Figures 1–4).

Figure 2.

Phase-encoded fMRI paradigm for visual field map measurements. (A) An example phase-encoded experimental design [87]. Top diagram shows the components of a single block of one stimulus presentation (teal) for one position (i.e., stimulus phase) of an expanding ring stimulus composed of a black and white moving checkerboard pattern. Middle diagram shows six blocks (teal) grouped together into one stimulus cycle (purple). The progressively larger ring stimulus is shown above each block. Each block in one cycle presents a specific stimulus in the “phase-encoded”—or “traveling wave”—sequence. Each cycle then repeats the same set of blocks. Lower diagram shows a full, single scan comprising six cycles; each cycle is one purple block. (B) Schematic diagram of three example phase-encoded time series with different stimulus responses. Each row depicts the time series measurement for a single voxel arising from a single, six-cycle scan using one experimental stimulus (e.g., polar angle). Simulated raw data points showing percent blood-oxygen-level-dependent (BOLD) modulation (i.e., response amplitude) are denoted by the black dots. The teal dotted lines represent sinusoidal fits of the simulated data points; each teal line characterizes the average BOLD activation in a different example voxel. The red lines indicate the peak activations per cycle for this imaginary set of voxels. Top and middle rows represent time series of voxels with the same %BOLD modulation, but different timing of peak responses, which indicates different stimulus selectivity (i.e., responses to different “phases” of the stimulus). Note the offset of the red lines between the two rows. For example, the top row might represent a voxel with a preferred eccentricity tuning of 2° eccentric to fixation, whereas the middle row might have a preferred tuning of 5° eccentric to fixation. Middle and bottom rows represent time series of voxels with the same timing of peak responses, indicating matching stimulus selectivity; i.e., both might have a preferred eccentricity tuning of 5° eccentric to fixation. However, the bottom row has much lower %BOLD modulation than the middle row. Such a difference in response amplitude can be due to several factors, such as differences in local vasculature or receptive field tuning. (C) Diagram of three example Fourier power spectra corresponding to the schematic time series in (B). In the phase-encoded paradigm, only BOLD responses that match the stimulus frequency of six cycles per scan (red peak) are considered as data. The responses must also be above a predetermined statistical threshold, typically measured in coherence or percentage variance explained [3, 87, 92]. (D) Diagram of three example averaged stimulus cycles corresponding to the schematic Fourier spectra in (C) and to averages of the time series in (B). Each teal dotted line represents the sinusoidal fit for the average, while the peak activation is again marked by the red line. The timing of the peak of each averaged cycle is used to calculate the phase of the preferred stimulus independently for each voxel. Typical pseudocolor overlays on 3D or flattened brain renderings as shown in Figures 6 and 12 use color to denote cortical responses to this peak activation. Note how the top measurement has an earlier peak (red line) that corresponds to an earlier phase of the stimulus (i.e., an earlier presentation time in the cycle) while the middle and bottom measurements’ peaks are shifted to later in time (e.g., [3]). The bottom example has a lower %BOLD modulation than the other two schematics, but the same peak activation as the middle example. For additional discussion, see [1, 3, 87, 91, 92].

Figure 3.

Population receptive field modeling. The parameter estimation procedure for the population receptive field (pRF) model is shown as a flow chart [92]. The pRF parameters are estimated from time series measurements using a linear spatiotemporal model of the fMRI response, which is a reasonable approximation over a wide range of spatiotemporal conditions [94–96]. The neuronal model is estimated by finding the model parameters that best predict the fMRI measurements. (A) The pRF modeling analysis is applied to all voxels (1:n) within the defined region of interest (ROI; e.g., occipital lobe, V1). (B) Multiple models of the expected average receptive field of the neuronal population may be used. Most commonly, a two-dimensional Gaussian is estimated, which is defined by three parameters, x, y, and σ, where (x, y) denotes the pRF center within the visual field, and σ is the Gaussian spread (i.e., pRF radius). Note that these parameters are stimulus-referred in degrees of visual angle. (C) The example moving bar stimulus composed of a black and white moving checkerboard pattern moves systematically across visual space. (D) The overlap between the pRF and effective stimulus is determined. (E, F) Next, the predicted pRF response is calculated for a given pRF model and effective stimulus location. The time series model prediction (F) is estimated by convolving the pRF activation with a model of the hemodynamic response function (HRF) as in (E) [94, 97]. The goodness-of-fit is estimated by computing the residual sum of squares (RSS) between this prediction and the data (black dots) (G). (H) 1