PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Alzheimers Dis. Author manuscript; available in PMC Mar 14, 2012.
Published in final edited form as:
PMCID: PMC3303148
NIHMSID: NIHMS356990
Effects of Hypoperfusion in Alzheimer’s Disease
Benjamin P. Austin,1,2,3 Veena A. Nair,3 Timothy. B. Meier,4 Guofan Xu,2 Howard A. Rowley,3,6 Cynthia M. Carlsson,2,5,6 Sterling C. Johnson,2,5,6 and Vivek Prabhakaran3,4
1UW Cardiovascular Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706, USA
2Department of Veterans Affairs (VA) Geriatric Research, Education and Clinical Center (GRECC), Madison, WI, USA
3Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
4Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI 53706, USA
5Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
6Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
Address Correspondence to: Vivek Prabhakaran, M.D., Ph.D., University of Wisconsin, School of Medicine and Public Health, Department of Radiology, 600 Highland Avenue, Madison, WI 53792-3252, Tel: +1 608 265 5269, Fax: +1 608 265 4152, vprabhakaran/at/uwhealth.org
The role of hypoperfusion in Alzheimer’s disease (AD) is a vital component to understanding the pathogenesis of this disease. Disrupted perfusion is not only evident throughout disease manifestation, it is also demonstrated during the pre-clinical phase of AD (i.e., mild cognitive impairment) as well as in cognitively healthy persons at high-risk for developing AD due to family history or genetic factors. Studies have used a variety of imaging modalities (e.g., SPECT, MRI, PET) to investigate AD, but with its recent technological advancements and non-invasive use of blood water as an endogenous tracer, arterial spin labeling (ASL) MRI has become an imaging technique of growing popularity. Through numerous ASL studies, it is now known that AD is associated with both global and regional cerebral hypoperfusion and that there is considerable overlap between the regions implicated in the disease state (consistently reported in precuneus/posterior cingulate and lateral parietal cortex) and those implicated in disease risk. Debate exists as to whether decreased blood flow in AD is a cause or consequence of the disease. Nonetheless, hypoperfusion in AD is associated with both structural and functional changes in the brain and offers a promising putative biomarker that could potentially identify AD in its pre-clinical state and be used to explore treatments to prevent, or at least slow, the progression of the disease. Finally, given that perfusion is a vascular phenomenon, we provide insights from a vascular lesion model (i.e., stroke) and illustrate the influence of disrupted perfusion on brain structure and function and, ultimately, cognition in AD.
Keywords: Alzheimer’s disease, hypoperfusion, perfusion, stroke, mild cognitive impairment, ASL, MRI, vascular risk factors
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by gradual onset, progressive deterioration, and decreased regional cerebral blood flow (CBF) [1]. Indeed, vascular factors play a critical role in the pathogenesis of AD [23], and it is currently debated whether decreased CBF is a cause or a consequence of AD [1]. Perfusion deficiencies are present from the very early pre-clinical phases of AD (i.e., during mild cognitive impairment (MCI)) and persist well into the latest stages of the disease, demonstrating a pattern of increased hypoperfusion with disease development. This phenomenon, over time, yields catastrophic consequences on brain structure, function, and cognition, leaving the patient irreversibly impaired, especially in their memory faculties.
Although there is no cure for this devastating illness, identification of pre-symptomatic AD is necessary to explore treatments (pharmacological and non-pharmacological) that could potentially prevent or at least slow the progression of the disease. Thus, much research has been focused on identifying biomarkers associated with AD manifestation as well as biomarkers in individuals at high risk for developing AD. Among the most promising of these putative biomarkers are the well-documented abnormalities in CBF associated with AD and its development.
Investigating perfusion in AD, however, is no straightforward task, as decreased CBF is only one of many neuropathological characteristics associated with AD. Indeed, the co-occurrence of hypoperfusion, arterial plaques, neurofibrillary tangles, vascular amyloid deposits, atrophy, and stenosis complicate the investigation of any one neuropathological feature, and it becomes increasingly difficult to distinguish cause from consequence. Thus, in order to better understand the effects of perfusion disruption in AD, a vascular lesion model such as stroke that examines the simplest form of perfusion alteration can be examined in order to gain further insight.
The following review investigates the role of perfusion in the development of AD. After a brief review of the genetic and vascular risk factors associated with AD, we discuss 1) imaging methods used to measure perfusion, 2) the brain regions most frequently disrupted by hypoperfusion in both pre-clinical and progressive AD, 3) the effects of hypoperfusion on the structure and function of the brain in AD, and 4) the role of disrupted perfusion in aging and stroke and its relation to AD.
The prevalence of late-onset AD, which accounts for approximately 97% of AD cases, is highly associated with the presence of the ε4 allele of the apolipoprotein E (APOE) gene on chromosome 19. The presence of one copy of the ε4 allele, which is carried by about half of all patients with dementia [4], is reported to increase the likelihood of developing AD by fourfold while two copies of the ε4 allele may increase risk by ninefold [5]. This genetic factor (APOE4), however, is neither necessary nor sufficient to cause AD, and so it remains of critical importance to identify biomarkers associated with developing AD in high-risk groups.
Vascular factors are repeatedly implicated in the risk for developing AD [1]. Factors such as ischemic stroke, atherosclerosis, hypertension, diabetes, and cardiac disease have been reported to result in cerebrovascular disease and trigger AD pathology in older adults [69]. Hypercholesterolemia in midlife also can lead to AD and has been targeted as a potentially modifiable risk factor [10]. Animal studies suggest that amyloid-β deposition in the brain, a hallmark characteristic of AD, is stimulated by hypercholesterolemia [11] and may be modified with the use of lipid-lowering agents, such as statins [12]. A recent study in humans showed that simvastatin improved cognition in asymptomatic middle-aged adults with a parental history of AD without significantly changing CSF Aβ42 or total tau levels [13].
The vascular risk factors associated with AD, however, also play a fundamental role in the development of vascular dementia (VaD), which by current diagnostic criteria, is differentiated from AD by its vascular pathology and its abrupt clinical onset [1]. The diagnostic mutual exclusivity of these two dementias is further equivocated by evidence from epidemiological, neuropathological, clinical, pharmacological, and functional studies which report considerable overlap in the risk factors and pathological changes associated with AD and VaD [1]. In this light, recent AD studies have focused more on brain circulation abnormalities and have collectively found that such vascular factors, including hypoperfusion, are more commonly associated with AD than was previously thought [14].
In AD, perfusion (Cerebral blood flow, Cerebral blood volume (CBV)) has been measured using a number of different imaging modalities including magnetic resonance imaging (MRI), CT, single photon emission computed tomography (SPECT), and regional cerebral metabolism using 2-deoxy-2-[F-18]fluoro-D-glucose (FDG-PET). Perfusion in AD has also been investigated using dynamic perfusion computed tomography and transcranial Doppler, but the incidence of such studies is far lower [14]. For the past two decades, SPECT and FDG-PET have served as the mainstream imaging techniques for perfusion and metabolism studies in AD, respectively [14]. SPECT, despite its relatively low spatial resolution (~1cm), lends to a large number of applications [1517], while FDG-PET, with higher sensitivity and spatial resolution than SPECT, is better able to measure regional cerebral metabolism in low perfusion areas [14]. These techniques, however, require the use of exogenous radioactive tracers and are more expensive than the more recently developed perfusion-weighted MRI (PW-MRI) techniques. PW-MRI, as an alternative to nuclear imaging techniques, offers the benefits of 1) economic efficiency (PET may require a cyclotron in proximity which is expensive to maintain), 2) accessibility (most hospitals now have at least one MRI system used for clinical practice but rarely have a cyclotron), and 3) higher spatial accuracy (MRI systems have a spatial resolution of up to 0.1 mm while PET systems are only capable of 5-mm resolution). Indeed, PW-MRI is a powerful and promising brain imaging technique and is currently being used by many researchers investigating perfusion in AD.
PW-MRI techniques can be divided into two categories based on the type of contrast agent used. Techniques that use an exogenous contrast tracer (such as dynamic contrast enhancement imaging and dynamic susceptibility contrast [DSC]) fall into the dynamic perfusion imaging subcategory, and techniques that use an endogenous contrast tracer fall into the arterial spin labeling (ASL) subcategory [14]. Currently, the AD perfusion literature is dominated by studies using DSC MRI [14], but many researchers are now migrating towards the use of ASL MRI because it is completely non-invasive and poses less risk for the patient. ASL measures CBF directly by using magnetically-labeled arterial blood water as an endogenous tracer [18]. In addition, ASL can be used to investigate blood flow associated with task performance by using a subtractive method similar to that used in blood oxygen level-dependent (BOLD) functional MRI (fMRI) studies. ASL, though equal in sensitivity as the BOLD signal for detecting task-induced changes in local brain function, provides more quantitative information and has been shown to be more robust than BOLD-fMRI with reduced intra- and inter-subject variability [1920]. ASL is further divided into sub-classes based on the labeling method (continuous, pulsed, or velocity-dependent) and can quantify CBF in single slices or for the whole brain. With its many recent advancements, ASL is fast becoming a popular choice for AD researchers and is thus the modality of focus for the current review.
Numerous studies have used ASL perfusion MRI to investigate CBF in AD, MCI, and in individuals at high-risk for developing AD, i.e., those with a parental history of AD or with at least one copy of APOE4. While individuals with AD demonstrate a global decrease in blood flow (averaged 40%) compared to healthy controls [21], CBF reduction may be specific to certain regions of the brain. Indeed, research has shown that individuals with AD consistently demonstrate reduced CBF in regions of the precuneus and/or posterior cingulate and frequently in lateral parietal cortex [see 18 for a review]. Other regions associated with decreased CBF in AD compared to healthy controls include regions of the temporo-occipital and parieto-occipital association cortices [22] as well as bilateral inferior parietal regions [23], hippocampus and parahippocampal gyrus [21], and regions in the prefrontal cortex [24] including bilateral superior and middle frontal gyri [23]. Even in studies which include an atrophy correction for gray matter loss, individuals with AD persist at demonstrating reduced CBF in the right inferior parietal lobe extending into the bilateral posterior cingulate gyri, bilateral middle frontal gyri [23], posterior cingulate extending into the precuneus, inferior parietal cortex, left inferior lateral frontal and orbitofrontal cortex [25]. Also, perfusion measures have been shown to correlate with dementia severity in the parieto-occipital region [as measured by a subset of the Blessed Dementia Scale; 22] and parietal cortex along with the precuneus/posterior cingulate [as measured by the Mini-Mental State Examination; 24].
Interestingly, some studies report elevated blood flow in AD compared to healthy controls. Individuals with AD have been reported to demonstrate hyperperfusion, even after atrophy correction, in hippocampus, parahippocampal gyrus, temporal pole, superior temporal gyrus [26] and anterior cingulate [2526]. Hyperperfusion in the prefrontal cortex may serve as a compensation mechanism, especially in the early stages of disease [14]. As for hyperperfusion in the hippocampal regions, it should be noted that increased blood flow to this region is in contrast to the previously discussed hypoperfusion [see 21 above]. Perhaps this discrepancy is attributable to differences in patient demographics as the study reporting hyperperfusion investigated individuals with AD of unspecified severity and a mean age of 75.6±9.2 yrs [26] whereas the study reporting hypoperfusion investigated individuals with mild AD and a mean age 70.7±8.7 yrs [21].
Studies of patients with MCI, a condition of memory impairment considered to be the clinical transition stage between normal aging and dementia [2728], provide insight into the prodromal phases of AD. Investigations of brain perfusion in individuals with MCI show that this group, in comparison to a healthy control group, demonstrates a reduction of CBF in the posterior cingulate with extension to the medial precuneus [atrophy corrected CBF; 25] as well as in right inferior parietal lobe (IPL) [23]. In the study by Johnson et al. [23], which compared individuals with AD and MCI to healthy controls, decreased perfusion in IPL was observed in both the AD and MCI groups but was more significantly reduced in the AD group. Compared to the MCI group, the AD group also demonstrated greater hypoperfusion in bilateral precuneus/posterior cingulate and bilateral inferior parietal lobe [23].
Hyperperfusion of certain brain regions has also been reported in MCI and other high-risk groups. MCI has been associated with increased blood flow in left hippocampus, right amygdala, and right basal ganglia compared to healthy controls [25]. Non-symptomatic high-risk groups also demonstrate hyperperfusion in the hippocampus; middle aged (average 58.5 years) individuals with a parental history of AD and at least one copy of APOE4 showed an approximately 25% elevated blood flow in the hippocampus compared to non-high-risk subjects [29].
Recent studies suggest that MCI may be a clinically heterogeneous syndrome [30]. Chao et al. [31] examined this idea by investigating CBF differences in two groups of single-domain MCI patients - those with isolated memory impairments (amnestic MCI) and those with isolated executive dysfunction impairments (dysexecutive MCI). Both groups demonstrated hypoperfusion in posterior cingulate compared to healthy controls, but individuals with dysexecutive MCI had significantly lower perfusion in left middle frontal gyrus, left posterior cingulate, and left precuneus when compared to individuals with amnestic MCI [31]. In another study investigating CBF during rest and during a memory encoding task, amnestic MCI patients demonstrated hypoperfusion in right precuneus and cuneus during the control state which extended to the posterior cingulate during task performance. Interestingly, healthy controls demonstrated a significant increase in perfusion in the parahippocampal gyrus when comparing task to baseline rest, but this increase was not observed in the MCI group. This suggests that individuals with amnestic MCI may lack the dynamic capability to modulate regional CBF in response to task demands [32]. In summary, research consistently demonstrates reduced CBF in posterior cingulate in MCI which could be a promising region for early detection [18].
The pathway leading to AD genesis is marked not only by CBF deficiency but also by structural changes observed in AD and MCI, which are debated by some to be a consequence of primary hypoperfusion (see [33] or [1] for an extensive review). Numerous studies have utilized voxel-based morphometry (VBM), a fully-automated technique that allows the quantifiable investigation of structures across the whole brain [34], to investigate atrophy in the brains of patients with AD and MCI. Collectively, these studies report numerous regions of cell death that are either specific to the pre-clinical phase (MCI), specific to disease manifestation, or that overlap both groups. Studies of patients with AD report atrophy of the entire hippocampus and regions of the temporal lobe, cingulum, precuneus, insular cortex, caudate nucleus, amygdala, entorhinal cortex, medial thalamus, and frontal cortex [3539]. Studies of patients with MCI report atrophy of the parahippocampal gyrus and medial temporal lobe [40], entorhinal cortex and cingulum [41], and insula and thalamus [37].
Patients with MCI, especially of the amnestic type, can be divided longitudinally by those who progress to AD and those who do not, and these groups show differential atrophy. Studies show that over time, patients with amnestic MCI who eventually progress to AD demonstrate gray matter loss in the medial and inferior temporal lobes, the temporoparietal neocortex, posterior cingulate, precuneus, anterior cingulate, and frontal lobes compared to amnestic MCI patients who are clinically stable [42]. Atrophy, however, is not exclusive to memory-impaired MCI patients; brain volume changes are also observed in cognitively healthy individuals. Studies report that individuals with a parental history of late-onset AD demonstrate decreased gray matter volume in precuneus, middle frontal, inferior frontal, and superior frontal gyri compared to individuals without a parental history of AD. Also, persons carrying the APOE4 allele have been reported to demonstrate decreased volume in hippocampus and amygdala compared to those without the APOE4 allele [4344].
Hypoperfusion may also lead to changes in cortical thickness as obtained from structural MRI scans. Cortical thickness measures are significant predictors of evolution to AD for subjects with MCI [45]. Carriers of the APOE4 allele, a demographic with reported decreased glucose metabolism in medial temporal and parietal lobes [4647], demonstrate accelerated cortical thinning in areas most vulnerable to aging (medial prefrontal and pericentral cortices) as well as in areas associated with AD and amyloid-aggregation (e.g., occipitotemporal and basal temporal cortices) [48]. Also, these carriers demonstrate significantly reduced cortical thickness in the entorhinal cortex when compared to non-carriers [49]. There is some evidence that the APOE4 allele has a stronger effect on cognitive decline in the earlier stages of AD and is less severe in the later stages [50].
In addition to abnormal perfusion and structural changes, individuals with AD also demonstrate functional changes in the brain. Functional connectivity is the temporal dependence of neuronal activity patterns of anatomically separated brain regions [5152]. This phenomenon can be investigated using MRI BOLD signal which is collected during rest (i.e., the signal is not driven by task performance). Although resting functional connectivity, or resting fMRI, methodologies are relatively new compared to those developed in task-driven fMRI, research has already yielded significant findings in AD. Because of the network-wide changes demonstrated as a result of local structural changes, AD is considered to be a disconnection syndrome [53]. During rest, patients with AD demonstrate decreased functional connectivity in both the default mode network (DMN) and the dorsal visuo-spatial system. The default mode network describes a set of brain regions that demonstrate decreased activation during task performance [5457], i.e., these regions demonstrate high BOLD activity and a high degree of intrinsic functional connectivity during rest and are “deactivated” during task- or stimulus-driven activity [5862]. The regions of the DMN include both medial (anterior and posterior cortical midline regions such as the ventromedial prefrontal cortex, the dorsomedial prefrontal cortex, different parts of the anterior cingulate cortex, the posterior cingulate cortex and precuneus) and lateral brain regions (lateral parietal cortex and hippocampus) [59].
Functional connectivity in AD as measured by resting fMRI may vary with severity of symptoms. Zhang et al. [63] investigated resting activity in three separate AD groups – those with mild, moderate, and severe AD. Their results show that all three groups demonstrated dissociated functional connectivity between the posterior cingulate cortex (PCC) and a set of other regions including bilateral visual cortices, inferior temporal cortex, hippocampus, and especially medial prefrontal cortex and precuneus/cuneus. Interestingly, the disruption of these various networks involving PCC intensified with increasing severity of AD. It should also be noted that certain regions (extending from left lateralized frontoparietal regions and spreading to bilateral frontoparietal regions) demonstrated increased connectivity to PCC with increasing severity of AD [63].
Aging is the leading risk factor for the development of late-onset AD. Investigation of the normal aging brain and age-related changes in vasculature serves as a fundamental template on which to better understand the pathogenesis of AD and its effect on cognition. Evidence from aging and stroke studies suggest that chronic brain hypoperfusion (CBH) leads to tissue pathology and cognitive impairments that are characteristic of AD.
With normal aging, cerebral vasculature undergoes both structural and functional changes that may act as a catalyst for cerebrovascular diseases and subsequent cognitive deficits. For example, changes in vascular ultrastructure, vascular reactivity, resting cerebral blood flow (rCBF) and oxygen metabolism are all associated with age [64]. There is also evidence that aging, per se, in the absence of other risk factors, promotes thickening and stiffness of the arteries and increases the morbidity and mortality of myocardial infarction and stroke [6566]. Perfusion studies have shown that in normal aging, uncomplicated by the presence of hypertension, diabetes, arteriosclerosis or dementia, there is evidence for decreased CBF, CBV, cerebral metabolic rate for oxygen (CMRO2), and glucose oxidation without significant change in oxygen extraction or blood brain barrier permeability [e.g., 67]. Also, these changes in CBF and CMRO2 have been found to be largely restricted to discrete brain regions presumed to be associated with cell loss [68].
Studies have documented that CBF decreases with age, either globally or in a region specific manner. In an ASL study, Bertsch et al. demonstrated an association between the age-dependent decline in global rCBF and performance in an attention task [69], but other studies suggest that declines in perfusion may be more region-specific. For example, brain regions critical to higher-cognition, such as the frontal cortex, the medial temporal lobe, and the cingulate gyrus display local age-related decreases in rCBF, even after controlling for partial volume effects [7071].
In addition to decreasing the volume of blood flow, age-related changes in cerebral vasculature can significantly alter the speed of blood flow during task performance. In a functional transcranial Doppler ultrasound study measuring cerebral blood flow velocities (BFV) in the ACA (anterior cerebral artery) and PCA (posterior cerebral artery), Sorond et al. found differences in BFV in healthy young and old adults during a word stem completion and a visual search task. In the younger subjects, greater activation was observed in the ACA than in the PCA territories during the word task, but older subjects did not show the same pattern. During the visual search task, however, both younger subjects and older subjects showed greater activation in PCA than in the ACA territories [72]. This suggests that blood flow to frontal areas may be altered in some cognitive tasks as part of the aging process.
Neurovascular and physiological changes associated with normal aging are often reflected in behavioral differences between the young and old. Studies show that older adults tend to display a general slowing in processing speed, a reduction in inhibitory control, and a general decline of attentional resources [7375]. Models of neurocognitive aging based on neuroimaging studies suggest that during task performance, older adults recruit additional brain regions compared to younger adults due to the effects of age on brain integrity and function [7677].
The relationship between perfusion and cognition in older adults raises the question of whether hyperperfusion can serve as compensatory mechanism against the cognitive decline seen in normal aging. It is known that exercise promotes healthy cognitive aging [78]. Conversely, Mozolic et al. have recently demonstrated that cognitive training increases rCBF in the rostrolateral PFC in older adults, and that this increase in rCBF correlated with the increase in their attention task [79]. Interestingly, models of neurocognitive aging suggest that the PFC is the seat of compensatory recruitment in older adults [7677] and sometimes in MCI. In an fMRI study in which MCI patients were divided into two groups based on Mattis Dementia Rating Scale scores, higher-cognition MCI patients activated right ventrolateral and dorsolateral prefrontal cortex during verbal memory tasks while lower-cognition MCI patients and control subjects did not [80]. This suggests that PFC compensation is present at the beginning of the MCI continuum but eventually breaks down as the symptoms increase in severity. This is similar to the hypothesis that PFC hyperperfusion is compensatory in early stages of AD [14]. Another interesting connection between perfusion in normal aging and in AD was noted in a study by Lee et al. [81]. In this study, cognitively normal elderly individuals displayed cortical thinning and hypoperfusion, as measured by ASL, in patterns similar to those observed in AD.
Animal studies have shown that reduced CBF over long durations, or chronic brain hypoperfusion (CBH), leads to neurochemical, metabolic, anatomic [e.g., 33, 82, 8389], and cognitive changes that are very similar to that observed in AD [90]. Aged rats that were subjected to 1–2 weeks of 2-vessel occlusion showed behavioral, physiological and anatomical changes. A significant finding was that the age of the animal together with the severity of CBH determined whether reperfusion could aid the animals to recover the CBF levels that existed prior to vessel occlusion. Based on evidence from rat CBH studies, de la Torre [85] suggests that aging combined with a vascular risk factor can lead to CBH which can, upon falling below a certain threshold (i.e., the critically attained threshold of cerebral hypoperfusion [CATCH]), trigger hemodynamic changes in the brain micro-circulature and impair optimal delivery of glucose and oxygen needed for normal brain cell function. Because glucose and oxygen are the crucial substrates in the production of tissue energy, metabolic energy deficits can trigger an intracellular biochemical cascade that effectively compromises brain cells and eventually leads to metabolic, cognitive and tissue pathology that characterize AD [9192].
There is strong evidence for hypoperfusion and the occurrence of brain ischemia and infarcts. Patients with severe arterial stenosis, or narrowing of the arteries, often show the presence of microemboli that fail to get washed out on account of reduced blood flow [93]. Hypoperfusion also leads to sub-optimal supply of nutrients to places that may be blocked by the emboli. It has been suggested that the degree of severity of stenosis is accompanied by differential rates of transient ischemic attacks (TIA; an episode of stroke-like symptoms lasting less than 24 hours) and strokes. Stroke patients in the acute and subacute phase show region specific hypoperfusion leading to cognitive deficits [9496]. There is also evidence that chronic stroke patients show hypoperfusion associated with cognitive deficits although without accompanying structural infarcts as indicated by T1- or T2-weighted scans [9799]. This suggests that functional areas receive enough blood supply so that tissue viability is sustained but not enough to support cognitive or neurological functioning [100]. Twenty to twenty-five percent of ischemic stroke patients go on to develop post-stroke dementia, especially in patients who are 55 years or older [101]. Patients with post-stroke dementia show changes in cerebral blood flow, white matter hyperintensities, and cortical thinning associated with varying degrees of cognitive impairments.
Although the evidence of a direct relationship between brain hypoperfusion and micro- or macro-structural changes leading to cognitive impairments is still forthcoming, recent studies suggest that the animal model of CBH proposed by de la Tore and colleagues [33] may very likely hold true in humans too. The thesis that CBH is a key determinant of eventual cognitive impairment is borne out of studies that have demonstrated that reperfusion of hypoperfused but dysfunctional regions leads to better functional outcomes [e.g., 102]. We therefore suggest that compromised vasculature may be represented on a continuum with mild vasculopathy falling at one end of this continuum (such as the vascular changes seen with aging), followed by moderate vasculopathy (such as those seen in patients with TIA or MCI) and severe vasculopathy (as noted in patients with stroke, VaD, or AD) falling at the other end of this continuum (see Figure 1). Chronic brain hypoperfusion may lead to micro- and macro-structural changes that are associated with cognitive impairments and dementia. However, the acuity of onset of these hypoperfusion changes contributes to the varying presentations of clinical disease in these population subsets.
Figure 1
Figure 1
Schematic figure of a perfusion model of chronic brain hypoperfusion (CBH) and micro- and macro-structural changes leading to behavioral deficits and cognitive impairment. The triangle represents a vasculature compromised in varying degrees with accompanying (more ...)
Although much is known about the role of hypoperfusion in AD, the direct consequences of disrupted blood flow is obscured by the co-occurrence of other neuropathological features implicated in AD including, e.g., arterial plaques, neurofibrillary tangles, vascular amyloid deposits, and cortical atrophy. Models of vascular lesion patients, however, provide a suitable model in which to investigate this phenomenon and to help elucidate the effects of decreased perfusion on cognition in AD. With the development of more economically efficient and non-invasive imaging techniques, such as ASL MRI, researchers are now able to measure blood flow with unprecedented spatial accuracy and with minimal risk to the patient. Thus, it is hopeful that in the near future, scientists will be able to identify putative biomarkers in AD and develop treatments to prevent, or at least slow, the progression of this incurable disease.
1. Mazza M, Marano G, Traversi G, Bria P, Mazza S. Primary cerebral blood flow deficiency and Alzheimer’s disease: shadows and lights. J Alzheimers Dis. 2011;23:375–389. [PubMed]
2. Dede DS, Yavuz B, Yavuz BB, Cankurtaran M, Halil M, Ulger Z, Cankurtaran ES, Aytemir K, Kabakci G, Ariogul S. Assessment of endothelial function in Alzheimer’s disease: is Alzheimer’s disease a vascular disease? J Am Geriatr Soc. 2007;55:1613–1617. [PubMed]
3. Zhu X, Smith MA, Honda K, Aliev G, Moreira PI, Nunomura A, Casadesus G, Harris PL, Siedlak SL, Perry G. Vascular oxidative stress in Alzheimer disease. J Neurol Sci. 2007;257:240–246. [PMC free article] [PubMed]
4. Eschweiler GW, Leyhe T, Kloppel S, Hull M. New developments in the diagnosis of dementia. Dtsch Arztebl Int. 2010;107:677–683. [PubMed]
5. Carlsson CM, Gleason CE, Puglielli L, Asthana S. Chapter 65. Dementia Including Alzheimer’s Disease. In: Halter JB, Ouslander JG, Tinetti ME, Studenski S, High KP, Asthana S, editors. Hazzard’s Geriatric Medicine and Gerontology. McGraw-Hill Medical; New York: 2009. pp. 797–811.
6. de la Torre JC. Cerebrovascular and cardiovascular pathology in Alzheimer’s disease. Int Rev Neurobiol. 2009;84:35–48. [PubMed]
7. Morovic S, Jurasic MJ, Martinic Popovic I, Seric V, Lisak M, Demarin V. Vascular characteristics of patients with dementia. J Neurol Sci. 2009;283:41–43. [PubMed]
8. Skoog I, Gustafson D. Hypertension, hypertension-clustering factors and Alzheimer’s disease. Neurol Res. 2003;25:675–680. [PubMed]
9. Viswanathan A, Rocca WA, Tzourio C. Vascular risk factors and dementia: how to move forward? Neurology. 2009;72:368–374. [PMC free article] [PubMed]
10. Kivipelto M, Helkala EL, Laakso MP, Hanninen T, Hallikainen M, Alhainen K, Iivonen S, Mannermaa A, Tuomilehto J, Nissinen A, Soininen H. Apolipoprotein E epsilon 4 allele, elevated midlife total cholesterol level, and high midlife systolic blood pressure are independent risk factors for late-life Alzheimer disease. Ann Intern Med. 2002;137:149–155. [PubMed]
11. Refolo LM, Malester B, LaFrancois J, Bryant-Thomas T, Wang R, Tint GS, Sambamurti K, Duff K, Pappolla MA. Hypercholesterolemia accelerates the Alzheimer’s amyloid pathology in a transgenic mouse model. Neurobiol Dis. 2000;7:321–331. [PubMed]
12. Fassbender K, Simons M, Bergmann C, Stroick M, Lutjohann D, Keller P, Runz H, Kuhl S, Bertsch T, von Bergmann K, Hennerici M, Beyreuther K, Hartmann T. Simvastatin strongly reduces levels of Alzheimer’s disease beta -amyloid peptides Abeta 42 and Abeta 40 in vitro and in vivo. Proc Natl Acad Sci U S A. 2001;98:5856–5861. [PubMed]
13. Carlsson CM, Gleason CE, Hess TM, Moreland KA, Blazel HM, Koscik RL, Schreiber NT, Johnson SC, Atwood CS, Puglielli L, Hermann BP, McBride PE, Stein JH, Sager MA, Asthana S. Effects of simvastatin on cerebrospinal fluid biomarkers and cognition in middle-aged adults at risk for Alzheimer’s disease. J Alzheimers Dis. 2008;13:187–197. [PubMed]
14. Chen W, Song X, Beyea S, D’Arcy R, Zhang Y, Rockwood K. Advances in perfusion magnetic resonance imaging in Alzheimer’s disease. Alzheimers Dement 2010 [PubMed]
15. DeKosky ST, Shih WJ, Schmitt FA, Coupal J, Kirkpatrick C. Assessing utility of single photon emission computed tomography (SPECT) scan in Alzheimer disease: correlation with cognitive severity. Alzheimer Dis Assoc Disord. 1990;4:14–23. [PubMed]
16. Holman BL, Tumeh SS. Single-photon emission computed tomography (SPECT). Applications and potential. JAMA. 1990;263:561–564. [PubMed]
17. Wintermark M, Sesay M, Barbier E, Borbely K, Dillon WP, Eastwood JD, Glenn TC, Grandin CB, Pedraza S, Soustiel JF, Nariai T, Zaharchuk G, Caille JM, Dousset V, Yonas H. Comparative overview of brain perfusion imaging techniques. Stroke. 2005;36:e83–99. [PubMed]
18. Alsop DC, Dai W, Grossman M, Detre JA. Arterial spin labeling blood flow MRI: its role in the early characterization of Alzheimer’s disease. J Alzheimers Dis. 2010;20:871–880. [PMC free article] [PubMed]
19. Aguirre GK, Detre JA, Zarahn E, Alsop DC. Experimental design and the relative sensitivity of BOLD and perfusion fMRI. Neuroimage. 2002;15:488–500. [PubMed]
20. Tjandra T, Brooks JC, Figueiredo P, Wise R, Matthews PM, Tracey I. Quantitative assessment of the reproducibility of functional activation measured with BOLD and MR perfusion imaging: implications for clinical trial design. Neuroimage. 2005;27:393–401. [PubMed]
21. Asllani I, Habeck C, Scarmeas N, Borogovac A, Brown TR, Stern Y. Multivariate and univariate analysis of continuous arterial spin labeling perfusion MRI in Alzheimer’s disease. J Cereb Blood Flow Metab. 2008;28:725–736. [PMC free article] [PubMed]
22. Sandson TA, O’Connor M, Sperling RA, Edelman RR, Warach S. Noninvasive perfusion MRI in Alzheimer’s disease: a preliminary report. Neurology. 1996;47:1339–1342. [PubMed]
23. Johnson NA, Jahng GH, Weiner MW, Miller BL, Chui HC, Jagust WJ, Gorno-Tempini ML, Schuff N. Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. Radiology. 2005;234:851–859. [PMC free article] [PubMed]
24. Alsop DC, Detre JA, Grossman M. Assessment of cerebral blood flow in Alzheimer’s disease by spin-labeled magnetic resonance imaging. Ann Neurol. 2000;47:93–100. [PubMed]
25. Dai W, Lopez OL, Carmichael OT, Becker JT, Kuller LH, Gach HM. Mild cognitive impairment and alzheimer disease: patterns of altered cerebral blood flow at MR imaging. Radiology. 2009;250:856–866. [PMC free article] [PubMed]
26. Alsop DC, Casement M, de Bazelaire C, Fong T, Press DZ. Hippocampal hyperperfusion in Alzheimer’s disease. Neuroimage. 2008;42:1267–1274. [PMC free article] [PubMed]
27. Morris JC, Cummings J. Mild cognitive impairment (MCI) represents early-stage Alzheimer’s disease. J Alzheimers Dis. 2005;7:235–239. discussion 255–262. [PubMed]
28. Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P. Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: an fMRI study. Hum Brain Mapp. 2005;26:231–239. [PubMed]
29. Fleisher AS, Podraza KM, Bangen KJ, Taylor C, Sherzai A, Sidhar K, Liu TT, Dale AM, Buxton RB. Cerebral perfusion and oxygenation differences in Alzheimer’s disease risk. Neurobiol Aging. 2009;30:1737–1748. [PMC free article] [PubMed]
30. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, Belleville S, Brodaty H, Bennett D, Chertkow H, Cummings JL, de Leon M, Feldman H, Ganguli M, Hampel H, Scheltens P, Tierney MC, Whitehouse P, Winblad B. Mild cognitive impairment. Lancet. 2006;367:1262–1270. [PubMed]
31. Chao LL, Pa J, Duarte A, Schuff N, Weiner MW, Kramer JH, Miller BL, Freeman KM, Johnson JK. Patterns of cerebral hypoperfusion in amnestic and dysexecutive MCI. Alzheimer Dis Assoc Disord. 2009;23:245–252. [PMC free article] [PubMed]
32. Xu G, Antuono PG, Jones J, Xu Y, Wu G, Ward D, Li SJ. Perfusion fMRI detects deficits in regional CBF during memory-encoding tasks in MCI subjects. Neurology. 2007;69:1650–1656. [PubMed]
33. de la Torre JC. Critical threshold cerebral hypoperfusion causes Alzheimer’s disease? Acta Neuropathol. 1999;98:1–8. [PubMed]
34. Kakeda S, Korogi Y. The efficacy of a voxel-based morphometry on the analysis of imaging in schizophrenia, temporal lobe epilepsy, and Alzheimer’s disease/mild cognitive impairment: a review. Neuroradiology. 2010;52:711–721. [PubMed]
35. Busatto GF, Garrido GE, Almeida OP, Castro CC, Camargo CH, Cid CG, Buchpiguel CA, Furuie S, Bottino CM. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease. Neurobiol Aging. 2003;24:221–231. [PubMed]
36. Frisoni GB, Testa C, Zorzan A, Sabattoli F, Beltramello A, Soininen H, Laakso MP. Detection of grey matter loss in mild Alzheimer’s disease with voxel based morphometry. J Neurol Neurosurg Psychiatry. 2002;73:657–664. [PMC free article] [PubMed]
37. Karas GB, Scheltens P, Rombouts SA, Visser PJ, van Schijndel RA, Fox NC, Barkhof F. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage. 2004;23:708–716. [PubMed]
38. Hirata Y, Matsuda H, Nemoto K, Ohnishi T, Hirao K, Yamashita F, Asada T, Iwabuchi S, Samejima H. Voxel-based morphometry to discriminate early Alzheimer’s disease from controls. Neurosci Lett. 2005;382:269–274. [PubMed]
39. Karas GB, Burton EJ, Rombouts SA, van Schijndel RA, O’Brien JT, Scheltens P, McKeith IG, Williams D, Ballard C, Barkhof F. A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. Neuroimage. 2003;18:895–907. [PubMed]
40. Visser PJ, Scheltens P, Verhey FR, Schmand B, Launer LJ, Jolles J, Jonker C. Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment. J Neurol. 1999;246:477–485. [PubMed]
41. Chetelat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron JC. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport. 2002;13:1939–1943. [PubMed]
42. Whitwell JL, Przybelski SA, Weigand SD, Knopman DS, Boeve BF, Petersen RC, Jack CR., Jr 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain. 2007;130:1777–1786. [PMC free article] [PubMed]
43. Honea RA, Swerdlow RH, Vidoni ED, Goodwin J, Burns JM. Reduced gray matter volume in normal adults with a maternal family history of Alzheimer disease. Neurology. 2010;74:113–120. [PMC free article] [PubMed]
44. Lu PH, Thompson PM, Leow A, Lee GJ, Lee A, Yanovsky I, Parikshak N, Khoo T, Wu S, Geschwind D, Bartzokis G. Apolipoprotein E genotype is associated with temporal and hippocampal atrophy rates in healthy elderly adults: a tensor-based morphometry study. J Alzheimers Dis. 2011;23:433–442. [PMC free article] [PubMed]
45. Querbes O, Aubry F, Pariente J, Lotterie JA, Demonet JF, Duret V, Puel M, Berry I, Fort JC, Celsis P. Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain. 2009;132:2036–2047. [PMC free article] [PubMed]
46. Reiman EM, Caselli RJ, Yun LS, Chen K, Bandy D, Minoshima S, Thibodeau SN, Osborne D. Preclinical evidence of Alzheimer’s disease in persons homozygous for the epsilon 4 allele for apolipoprotein E. N Engl J Med. 1996;334:752–758. [PubMed]
47. Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, Saunders AM, Hardy J. Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proc Natl Acad Sci U S A. 2004;101:284–289. [PubMed]
48. Espeseth T, Westlye LT, Fjell AM, Walhovd KB, Rootwelt H, Reinvang I. Accelerated age-related cortical thinning in healthy carriers of apolipoprotein E epsilon 4. Neurobiol Aging. 2008;29:329–340. [PubMed]
49. Burggren AC, Zeineh MM, Ekstrom AD, Braskie MN, Thompson PM, Small GW, Bookheimer SY. Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers. Neuroimage. 2008;41:1177–1183. [PMC free article] [PubMed]
50. Cosentino S, Scarmeas N, Helzner E, Glymour MM, Brandt J, Albert M, Blacker D, Stern Y. APOE epsilon 4 allele predicts faster cognitive decline in mild Alzheimer disease. Neurology. 2008;70:1842–1849. [PMC free article] [PubMed]
51. Aertsen AM, Gerstein GL, Habib MK, Palm G. Dynamics of neuronal firing correlation: modulation of “effective connectivity” J Neurophysiol. 1989;61:900–917. [PubMed]
52. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab. 1993;13:5–14. [PubMed]
53. Delbeuck X, Van der Linden M, Collette F. Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev. 2003;13:79–92. [PubMed]
54. Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev. 2009;33:279–296. [PubMed]
55. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38. [PubMed]
56. Morcom AM, Fletcher PC. Does the brain have a baseline? Why we should be resisting a rest. Neuroimage. 2007;37:1073–1082. [PubMed]
57. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–682. [PubMed]
58. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005;360:1001–1013. [PMC free article] [PubMed]
59. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103:13848–13853. [PubMed]
60. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–9678. [PubMed]
61. Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp. 2005;26:15–29. [PubMed]
62. Greicius MD, Menon V. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J Cogn Neurosci. 2004;16:1484–1492. [PubMed]
63. Zhang HY, Wang SJ, Liu B, Ma ZL, Yang M, Zhang ZJ, Teng GJ. Resting brain connectivity: changes during the progress of Alzheimer disease. Radiology. 2010;256:598–606. [PubMed]
64. D’Esposito M, Jagust W, Gazzaley A. Methodological and Conceptual Issues in the Study of the Aging Brain. In: Jagust W, D’Esposito M, editors. Imaging the Aging Brain. Oxford University Press; New York: 2009. pp. 11–25.
65. Lakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part II: the aging heart in health: links to heart disease. Circulation. 2003a;107:346–354. [PubMed]
66. Lakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part I: aging arteries: a “set up” for vascular disease. Circulation. 2003b;107:139–146. [PubMed]
67. Dastur DK. Cerebral blood flow and metabolism in normal human aging, pathological aging, and senile dementia. J Cereb Blood Flow Metab. 1985;5:1–9. [PubMed]
68. Stoquart-ElSankari S, Baledent O, Gondry-Jouet C, Makki M, Godefroy O, Meyer ME. Aging effects on cerebral blood and cerebrospinal fluid flows. J Cereb Blood Flow Metab. 2007;27:1563–1572. [PubMed]
69. Bertsch K, Hagemann D, Hermes M, Walter C, Khan R, Naumann E. Resting cerebral blood flow, attention, and aging. Brain Res. 2009;1267:77–88. [PubMed]
70. Asllani I, Habeck C, Borogovac A, Brown TR, Brickman AM, Stern Y. Separating function from structure in perfusion imaging of the aging brain. Hum Brain Mapp. 2009;30:2927–2935. [PMC free article] [PubMed]
71. Beason-Held LL, Kraut MA, Resnick SM. Stability Of Default-Mode Network Activity In The Aging Brain. Brain Imaging Behav. 2009;3:123–131. [PMC free article] [PubMed]
72. Sorond FA, Schnyer DM, Serrador JM, Milberg WP, Lipsitz LA. Cerebral blood flow regulation during cognitive tasks: Effects of healthy aging. Cortex. 2008;44:179–184. [PMC free article] [PubMed]
73. Craik FI. Aging and cognitive deficits: The role of attentional resources. In: Craik FI, Trehub S, editors. Aging and cognitive processes. Plenum; New York: 1982. pp. 191–211.
74. Hasher L, Stoltzfus ER, Zacks RT, Rypma B. Age and inhibition. J Exp Psychol Learn Mem Cogn. 1991;17:163–169. [PubMed]
75. Salthouse TA. The processing-speed theory of adult age differences in cognition. Psychol Rev. 1996;103:403–428. [PubMed]
76. Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging. 2002;17:85–100. [PubMed]
77. Park DC, Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol. 2009;60:173–196. [PMC free article] [PubMed]
78. Rolland Y, Abellan van Kan G, Vellas B. Healthy brain aging: role of exercise and physical activity. Clin Geriatr Med. 2010;26:75–87. [PubMed]
79. Mozolic JL, Hayasaka S, Laurienti PJ. A cognitive training intervention increases resting cerebral blood flow in healthy older adults. Front Hum Neurosci. 2010;4:16. [PMC free article] [PubMed]
80. Clement F, Belleville S. Compensation and disease severity on the memory-related activations in mild cognitive impairment. Biol Psychiatry. 2010;68:894–902. [PubMed]
81. Lee C, Lopez OL, Becker JT, Raji C, Dai W, Kuller LH, Gach HM. Imaging cerebral blood flow in the cognitively normal aging brain with arterial spin labeling: implications for imaging of neurodegenerative disease. Journal of neuroimaging : official journal of the American Society of Neuroimaging. 2009;19:344–352. [PMC free article] [PubMed]
82. De Jong GI, Farkas E, Stienstra CM, Plass JR, Keijser JN, de la Torre JC, Luiten PG. Cerebral hypoperfusion yields capillary damage in the hippocampal CA1 area that correlates with spatial memory impairment. Neuroscience. 1999;91:203–210. [PubMed]
83. de la Torre JC. Hemodynamic consequences of deformed microvessels in the brain in Alzheimer’s disease. Ann N Y Acad Sci. 1997;826:75–91. [PubMed]
84. de la Torre JC. Cerebral hypoperfusion, capillary degeneration, and development of Alzheimer disease. Alzheimer Dis Assoc Disord. 2000a;14(Suppl 1):S72–81. [PubMed]
85. de la Torre JC. Critically attained threshold of cerebral hypoperfusion: can it cause Alzheimer’s disease? AnnN Y Acad Sci. 2000b;903:424–436. [PubMed]
86. de la Torre JC. Pathophysiology of neuronal energy crisis in Alzheimer’s disease. Neurodegener Dis. 2008;5:126–132. [PubMed]
87. de la Torre JC, Fortin T, Park GA, Butler KS, Kozlowski P, Pappas BA, de Socarraz H, Saunders JK, Richard MT. Chronic cerebrovascular insufficiency induces dementia-like deficits in aged rats. Brain Res. 1992a;582:186–195. [PubMed]
88. de la Torre JC, Fortin T, Park GA, Saunders JK, Kozlowski P, Butler K, de Socarraz H, Pappas B, Richard M. Aged but not young rats develop metabolic, memory deficits after chronic brain ischaemia. Neurol Res. 1992b;14:177–180. [PubMed]
89. de la Torre JC, Stefano GB. Evidence that Alzheimer’s disease is a microvascular disorder: the role of constitutive nitric oxide. Brain Res Brain Res Rev. 2000c;34:119–136. [PubMed]
90. Lee JS, Im DS, An YS, Hong JM, Gwag BJ, Joo IS. Chronic cerebral hypoperfusion in a mouse model of Alzheimer’s disease: An additional contributing factor of cognitive impairment. Neurosci Lett. 2011;489:84–88. [PubMed]
91. Kalaria RN. Blackwell Publishing Ltd. 1997. pp. 263–271. [PubMed]
92. Ongali B, Nicolakakis N, Lecrux C, Aboulkassim T, Rosa-Neto P, Papadopoulos P, Tong X-K, Hamel E. Transgenic Mice Overexpressing APP and Transforming Growth Factor-[beta]1 Feature Cognitive and Vascular Hallmarks of Alzheimer’s Disease. The American Journal of Pathology. 2010;177:3071–3080. [PubMed]
93. Caplan LR, Hennerici M. Impaired Clearance of Emboli (Washout) Is an Important Link Between Hypoperfusion, Embolism, and Ischemic Stroke. Arch Neurol. 1998;55:1475–1482. [PubMed]
94. Hillis AE, Wityk RJ, Barker PB, Beauchamp NJ, Gailloud P, Murphy K, Cooper O, Metter EJ. Subcortical aphasia and neglect in acute stroke: the role of cortical hypoperfusion. Brain. 2002;125:1094–1104. [PubMed]
95. Hillis AE, Wityk RJ, Beauchamp NJ, Ulatowski JA, Jacobs MA, Barker PB. Perfusion-weighted MRI as a marker of response to treatment in acute and subacute stroke. Neuroradiology. 2004;46:31–39. [PubMed]
96. Hillis AE, Wityk RJ, Tuffiash E, Beauchamp NJ, Jacobs MA, Barker PB, Selnes OA. Hypoperfusion of Wernicke’s area predicts severity of semantic deficit in acute stroke. Ann Neurol. 2001;50:561–566. [PubMed]
97. Krakauer JW, Radoeva PD, Zarahn E, Wydra J, Lazar RM, Hirsch J, Marshall RS. Hypoperfusion without stroke alters motor activation in the opposite hemisphere. Ann Neurol. 2004;56:796–802. [PubMed]
98. Love T, Swinney D, Wong E, Buxton R. Perfusion imaging and stroke: A more sensitive measure of the brain bases of cognitive deficits. Aphasiology. 2002;16:873–883.
99. Prabhakaran V, Raman SP, Grunwald MR, Mahadevia A, Hussain N, Lu H, Van Zijl PC, Hillis AE. Neural substrates of word generation during stroke recovery: the influence of cortical hypoperfusion. Behav Neurol. 2007;18:45–52. [PubMed]
100. Brumm KP, Perthen JE, Liu TT, Haist F, Ayalon L, Love T. An arterial spin labeling investigation of cerebral blood flow deficits in chronic stroke survivors. Neuroimage. 2010;51:995–1005. [PMC free article] [PubMed]
101. Leys D, Hénon H, Mackowiak-Cordoliani M-A, Pasquier F. Poststroke dementia. The Lancet Neurology. 2005;4:752–759. [PubMed]
102. Hillis AE, Kane A, Tuffiash E, Ulatowski JA, Barker PB, Beauchamp NJ, Wityk RJ. Reperfusion of specific brain regions by raising blood pressure restores selective language functions in subacute stroke. Brain Lang. 2001;79:495–510. [PubMed]