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Published online before print August 30, 2002, 10.1148/radiol.2251011301
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(Radiology 2002;225:253-259.)
© RSNA, 2002

Neuroradiology

Alzheimer Disease: Evaluation of a Functional MR Imaging Index as a Marker1

Shi-Jiang Li, PhD, Zhu Li, MD, Gaohong Wu, PhD, Mei-Jie Zhang, PhD, Malgorzata Franczak, MD and Piero G. Antuono, MD

1 From the Biophysics Research Institute (S.J.L., Z.L., G.W.), Division of BioStatistics (M.J.Z.), and Department of Neurology (M.F., P.G.A.), Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226. Received July 30, 2001; revision requested September 25; final revision received March 21, 2002; accepted April 12. Supported in part by the Extendicare Foundation, the Dana Foundation, and National Institutes of Health research grants DA10214, MH51358, and RR00058. Address correspondence to S.J.L. (e-mail: sjli@mcw.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
PURPOSE: To measure changes in functional synchrony in the hippocampus in patients with mild cognitive impairment (MCI) and Alzheimer disease (AD).

MATERIALS AND METHODS: Three subject groups (nine cognitively healthy elderly control subjects, 10 patients with probable AD, and five subjects with MCI) underwent resting-state functional magnetic resonance (MR) imaging for measurement of functional synchrony in the hippocampus. Functional synchrony was defined and quantified as the mean of the cross-correlation coefficients of spontaneous low frequency (COSLOF) components between possible pairs of voxel time courses in a brain region, or the COSLOF index. The two-tailed Student t test was used to determine differences in the COSLOF index between the control group, the probable AD group, and the MCI group. An operating characteristic curve was calculated to graphically depict the tradeoff between sensitivity and specificity of the COSLOF index.

RESULTS: Functional synchrony quantified with the COSLOF index was obtained in AD, MCI, and control subjects. COSLOF index values were significantly lower in AD patients than in control subjects (t = 4.32, P < .0012). For MCI subjects, COSLOF index values were significantly higher than those of AD patients (t = -2.4052, P < .047) but significantly lower than those of control subjects (t = 2.257, P < .043). The exponential-class curve significantly fits the relationship between the COSLOF index and the Mini-Mental Status Examination score ({chi}2 = 20.4), indicating the rapid decrease in cognitive capacity below a threshold of the COSLOF index.

CONCLUSION: Our results suggest that the COSLOF index could be used as a noninvasive quantitative marker for the preclinical stage of AD.

© RSNA, 2002

Index terms: Alzheimer disease, 10.83 • Brain, function, 10.83 • Brain, MR, 10.121412, 10.121416, 10.121419 • Magnetic resonance (MR), functional imaging, 10.121419


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Alzheimer disease (AD) is a progressive neurologic illness characterized by gradual deposition of neuritic plaques and neurofibrillary tangles in the human brain that is thought to occur decades before clinical symptoms are manifested (1–3). Identification of the people at risk before the clinical appearance of dementia has become a priority since they could benefit from therapeutic and preventive interventions. Those at greatest risk for the development of AD are individuals with mild cognitive impairment (MCI), who are identified clinically with neuropsychologic testing to determine isolated memory loss. MCI progresses to AD with a prevalence of 15% annually (4). At present there are no biologic markers for MCI or AD.

Several methods have been proposed to identify signs and establish biologic markers of the preclinical phase. Measurement of plasma concentration of the 42-residue ß-amyloid has been proposed as a preclinical marker (5). Genetic markers such as the PS1 and PS2 genes are predictive for a small number of cases of early-onset AD. The ApoE4 allele, present in 50%–75% of AD patients, can be used to predict when, but not if, a person is predisposed to develop AD (6,7). Hippocampal atrophy, measured with high-spatial-resolution magnetic resonance (MR) imaging of the brain, has been shown to have important diagnostic value (8,9). One disadvantage of these methods is their limited sensitivity and specificity (10).

The results of several functional neuroimaging studies with positron emission tomography (PET) and functional MR imaging suggest that compensatory functional responses may be present in asymptomatic subjects and in early AD (11,12). With disease progression, however, such a compensatory response may no longer be observable and, consequently, cannot provide reliable preclinical detection of AD because of its overlap with findings in control subjects.

Developments in functional MR imaging technology with high spatial and temporal resolutions made it possible to detect the spontaneous low-frequency fluctuation (SLF) (13,14), which has been studied for more than 40 years (15,16) and intensively reviewed (17). The SLF has also been used to map brain function (18) and to study brain lesions (19). Unlike conventional functional MR imaging, which typically requires stimulus paradigms to activate neuronal circuitry, the SLFs are obtained while a subject is at rest (rest here is relative to stimuli paradigms). We hypothesize that the gradual accumulation of AD lesions in the brain will affect the SLF signals and reduce their functional synchrony within brain regions. To determine functional synchrony, the cross-correlation coefficients between SLF of voxel time courses in a brain region are used. Since the hippocampus is one of the earliest loci affected by the accumulation of AD lesions, the functional synchrony in the hippocampus of subjects with MCI and early AD should be affected. Thus, the purpose of our study was to measure changes in functional synchrony in the hippocampus region in patients with MCI and in those with AD.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects and Cognitive Testing
Thirteen cognitively healthy elderly control subjects, 14 AD patients, and six subjects with MCI were consecutively recruited through the memory disorders clinic at our institution. Nine of these 33 subjects were excluded from functional MR imaging analysis owing to excessive motion. Completing the study were nine control subjects (three men with mean age of 70 years ± 3 [SD] and six women with mean age of 70 years ± 7), 10 AD patients (five men with mean age of 73 years ± 5 and five women with mean age of 71 years ± 10), and five MCI subjects (three men with mean age of 69 years ± 3 and two women with mean age of 66 years ± 2). The sample sizes in each group were determined according to statistical power calculation and were also adjusted to account for a potential dropout rate of 20%. All subjects were right handed, in good physical health, and free of ferrous or electronic implants. Written informed consent was obtained from each subject or guardian, and all experiments were performed in compliance with regulations of the institutional review board.

The diagnosis of probable AD was made on the basis of nationally accepted criteria (20). In addition, a score of 4 or less on the modified Hachinski Cerebral Ischemia Scale was used to exclude patients with major risk factors for vascular disease (21). Other exclusionary criteria included the presence of infarction or focal lesion on imaging studies acquired before the functional MR imaging study for all probable AD patients. Mini-Mental Status Examination (MMSE) scores were measured but were not considered as a factor for exclusion (22).

Criteria for MCI (4) consisted of the following: (a) subjective symptoms of memory loss, (b) normal activities of daily living, (c) normal general cognitive function, (d) impaired memory function for age as measured with a Rey Auditory Verbal Learning Test (<2 SDs), (e) absence of dementia, and (f) MMSE score of 24/30 or higher (22). The Clinical Dementia Rating Scale was used as an inclusion tool (23). The requirement for scale-removed sum of box scores was 0.5 or less. Subjects were required to have a modified Hachinski Cerebral Ischemia Scale score of 4 or less (21). These criteria were applied consistently to all subjects in this category.

All cognitively healthy subjects underwent a cognitive examination; none reported subjective symptoms of cognitive impairment, MMSE score of 27/30 or higher, or modified Hachinski score of 4 or less. Functional MR imaging was performed within a maximum of 2 months of cognitive testing.

MR Imaging and Functional MR Imaging
All experiments were conducted with a 1.5-T MR imager (Signa; GE Medical Systems, Milwaukee, Wis) with a three-axis balanced-torque local gradient coil and a shielded quadrature elliptic end-capped transmit or receive birdcage radio-frequency coil. Foam padding was used to limit head motion within the head coil. In all MR imaging sessions, localized T1-weighted transverse and sagittal images were acquired to provide structural information and to define the number of sections and location for functional MR imaging experiments. All experiments were performed in the sagittal plane with a gradient-recalled-echo (GRE) echo-planar MR imaging pulse sequence (2,000/40 [repetition time msec/echo time msec], in-plane resolution of 3.75 mm with 64 x 64 image matrix, field of view of 24 cm, section thickness of 7 mm). Each functional MR imaging session required 6 minutes to obtain 15 sagittal sections; each section contained 180 images. To reduce the aliasing effect with repetition time of less than 2 seconds, a crusher gradient was applied (24). Determination of the hippocampus region was conducted with a spoiled GRE sequence (24/5, flip angle of 45°, one signal acquired, section thickness of 1.1 mm). A total imaging time of 30 minutes, including functional MR imaging and spoiled GRE MR imaging, was required for each imaging session. A repeated 6-minute functional MR image was acquired in two control subjects and two AD patients for the test-retest experiments.

Functional MR Imaging and Postprocessing
All functional MR imaging data sets were subjected to a head-motion correction routine. The functional MR imaging data sets were registered among different time points with an auxiliary program in a software package (3dvolreg in Analysis of Functional Neuro-Imaging [25]). The movement parameters were checked. Subjects with excessive movement (>1 pixel) that was not correctable were excluded from further analysis. On the basis of findings in this procedure, the following subjects were excluded: four AD patients, one MCI subject, and four control subjects. Occasionally, a sharp spike in voxel time courses can be observed. Such spikes were detected and removed as follows: Assuming the mean signal intensity and its SD as µ and {sigma}, respectively, if a signal intensity is greater than µ + 4{sigma} or less than µ - 4{sigma}, and the length of these data points is two time points or less (four time points with repetition time of 2,000 msec), the intensity of the corresponding time points was then replaced with a weighted-average value of the adjacent time points of this period. All image postprocessing was performed by one author (G.W.).

Selection of Voxels in the Hippocampus and the Primary Visual Cortex
The T1-weighted MR images acquired with the spoiled GRE pulse sequence were processed with software (25). First, the software masks the entire hippocampus region, according to the brain atlas, on three-dimensional spoiled GRE images of the Talairach space and then excludes the white matter and CSF voxels (and therefore atrophic voxels), according to the intensity of the hippocampus histogram. To determine which voxel time courses should be included in the calculation of the coefficients of spontaneous low frequency (COSLOF) index, the first step is to determine which voxels are considered within the hippocampus. Since the spoiled GRE images had 1.1-mm spatial resolution and the functional MR images had 3.75-mm spatial resolution, the voxels included in the hippocampus region in functional MR images were determined according to the masked volume in the spoiled GRE images by using a deresolution program. (Note, functional MR images were also transformed into the Talairach space.) Only those voxels in functional MR images that contained more than 50% of masked spoiled GRE voxels were included for the voxel time course analysis. The same method was applied to the determination of voxels in the primary visual cortex. The voxels were selected by one author (Z.L.).

Calculation of the COSLOF Index
After the voxels in the hippocampus were selected, the corresponding voxel time courses were processed to obtain the SLF components. First, the linear trend that may be present in voxel time courses was removed by means of the linear regression method; then, the voxel time course was convolved with a Hamming bandpass filter. The expression of a Hamming filter (26) with a length of N and a passband between frequencies from fl to fh is the following:

where n = 0, 1, ... , N - 1 and N = 2M + 1. In our study, N is set to 9, and fl and fh are set to 0.015 and 0.10 Hz, respectively.

To quantify the functional synchrony within the hippocampus, we conducted cross correlation between these SLF components. The cross-correlation coefficient (ccij) between any two voxel time courses si(n) and sj(n) was calculated as follows:

where n = 0, 1, ... , N - 1, where N is the number of time points of the voxel time course, or 180 in our study. The s(n) represents the Hamming-filtered voxel time course, and its mean value is set to 0.

The COSLOF index defines the mean components from all possible pairs of filtered voxel time courses in a brain region. The COSLOF index is mathematically calculated from a pairwise correlation coefficient matrix:

where K is the number of voxels in the region of interest and ccij represents the cross-correlation coefficient between the ith and jth filtered voxel time courses. Specifically, assume the hippocampus is the region of interest and let K be the number of voxels in the region of interest. Let each filtered voxel time course si(n) be cross correlated with filtered voxel time course sj(n) of K voxels, which results in a cross-correlation coefficient matrix. This matrix has dimensionality of K x K, with entry of the ij element given by the cross-correlation coefficient of the ith and jth voxel time courses. Those cross-correlation coefficients at off-diagonal entries (i != j) in this matrix are symmetric, and their histogram distribution can be plotted as the cross-correlation coefficient versus its relative frequencies. A similar approach can be repeated for other brain regions such as the primary visual cortex.

Calculation of Receiver Operating Characteristic Curves
We calculated the receiver operating characteristic curves and SDs according to an asymptotic normality distribution and a simulation method (27,28), to graphically depict the trade-off between sensitivity and specificity of the COSLOF index test. When we calculated the receiver operating characteristic curves, the five MCI subjects and nine control subjects were grouped together and assumed as test negative; the 10 AD patients were assumed as test positive. The receiver operating characteristic curve is a standard summary method for displaying the accuracy of a test; it is a plot of the sensitivity versus 1 - specificity for all possible threshold values. In tests, the diseased and nondiseased states are often denoted as D+ and D-, respectively. If T denotes the diagnostic test with lower values more indicative of disease, then for possible threshold values of t, the sensitivity (true-positive fraction) and 1 - specificity (false-positive fraction) are P(T <= t | D+) and P(T <= t | D-), respectively. The higher the receiver operating characteristic curve, the better the test is for distinguishing subjects with disease from those without disease.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
DISCUSSION
 REFERENCES
 
Figure 1, A, is a representative sagittal T1-weighted image of the hippocampus obtained in a control subject. Figure 1, B, shows the voxel time course from the hippocampus (arrow) on a blood oxygen level–dependent functional MR image. Figure 1, C, shows the SLF signal that was obtained after the voxel time course underwent bandpass filtering.



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  		Figure 1. Extraction of an SLF component in a voxel time course from the human hippocampus in a 70-year-old male control subject. A, Sagittal T1-weighted MR image, acquired with the spoiled GRE pulse sequence (24/5, flip angle of 45°, section thickness of 1.1 mm), depicts the hippocampal region. B, Representative voxel time course obtained with GRE echo-planar MR imaging (2,000/40, section thickness of 7 mm, field of view of 24 cm, in-plane resolution of 3.75 mm with 64 x 64 image matrix). C, The corresponding SLF component extracted from the voxel time course. The numbers in B and C are image numbers (x axes) and arbitrary signal intensities (y axes). The arrow in B points to the voxel time course from the hippocampus region.

 
As demonstrated in Figure 2, the mean value of the histogram of cross-correlation coefficients from a probable AD patient is 0.12 and is lower than that (0.31) from a control subject. We repeated these measurements successfully in nine control subjects, 10 probable AD patients, and five MCI subjects. With use of the two-tailed Student t test, significant differences in COSLOF index values were found to exist between the control and probable AD groups (t = 4.292, P < .001), between MCI and AD groups (t = -3.050, P < .01), and between MCI and control groups (t = 2.201, P < .05). The COSLOF index values were lowest in AD patients (0.121 ± 0.004), highest in control subjects (0.308 ± 0.014), and in the range between lowest and highest in MCI subjects (0.208 ± 0.002).



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  		Figure 2. A, Representative image from the 12 sagittal spoiled GRE MR images (parameters in Figure 1) obtained in a 70-year-old male control subject contains a portion of the hippocampus, the white masked areas in the Talairach space. Hippocampus voxels for the calculation of the COSLOF index were determined with software (25). B, Histogram of cross-correlation coefficients obtained in the hippocampus region of this control subject ({square}) and an age-matched male probable AD patient ({blacksquare}). These two histograms are significantly different. The mean of the correlation coefficients is defined as the COSLOF index, which is used to quantitatively measure functional synchrony.

 
The COSLOF index was also measured in the primary visual cortex region. The histograms of cross-correlation coefficients within the visual cortex from the control subject and the AD patient have a large overlap (Fig 3). The COSLOF index values among the AD group, control group, and MCI group are not significantly different. This result is consistent with findings that the vision of probable AD patients is highly preserved until later phases of the illness (29,30).



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  		Figure 3. A, Representative sagittal spoiled GRE MR image (parameters in Figure 1), in the same control subject as in Figure 2, contains the primary visual cortex (white masked area). The primary visual cortex was determined on the basis of the gray matter bank, along with the calcarine fissure from the occipital pole to the intersection between the parieto-occipital sulcus and calcarine fissure (gray line in the white masked areas). B, Histograms of cross-correlation coefficients obtained from the primary visual cortex region in this control subject ({square}) and an age-matched male probable AD patient ({blacksquare}). These two histograms largely overlap each other and are not significantly different, which suggests less impaired functional synchrony in the visual cortex region in probable AD patients.

 
To test intrasubject repeatability, we performed test-retest experiments with two control subjects and two AD patients. The results from these two tests show that the COSLOF index measurement is stable and that there is less than 10% error between tests (Table). Absolute maximum changes in COSLOF index values between tests for each individual were 0.02. In contrast to small intrasubject variability, the intersubject variability is large among different groups of subjects (Fig 4).


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  		Results of Test-Retest Experiments

 


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  		Figure 4. Scatterplot shows that the exponential-class curve significantly fits the relationship between MMSE scores and the COSLOF index among nine control subjects ({circ}), 10 AD patients ({bullet}), and five MCI subjects ({triangleup}). The results suggest that the lower the COSLOF index value, the higher the risk for the development of AD. A further longitudinal study with MCI subjects is necessary to confirm such a suggestion.

 
All subjects in the study had cognitive function measured with the MMSE. COSLOF index values in the hippocampus significantly fit with the MMSE scores of the study population (exponential-class curve y = A(1 - e-k(x-b)), where A = 30, k = 23, b = 0.09 with {chi}2 = 20.4 (Fig 4). This exponential relationship between the MMSE scores and the COSLOF indexes indicates a rapid decrease in the cognitive capacity when the COSLOF index is below a threshold of 0.19.

The sensitivity and specificity of the COSLOF index test are shown with a receiver operating characteristic curve analysis in Figure 5. The area under the curve of the COSLOF index test is greater than that under the 45° diagonal line; the result is consistent with the significant difference between the AD group and control group with the t test.



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  		Figure 5. Line graph depicts the receiver operating characteristic curve with SDs (error bars) and shows the relationship between sensitivity and specificity of the COSLOF index test. On the basis of the curve, if we accept a 10% false-positive rate, then the COSLOF index test will provide an 80% true-positive rate. This test was conducted with 10 AD patients, five MCI subjects, and nine age-matched elderly control subjects.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
REFERENCES
 
The concept of functional synchrony is not new. It has been applied to electroencephalographic coherence (31), where it is a measure of the synchronization between two signals across distinct regions of human brain and is interpreted as an expression of their functional interaction. In PET studies, functional synchrony is defined as a spatial-temporal correlation between spatially distinct regions of cerebral cortex (32,33), and it is suggested that synchronous fluctuations may be a general cortical phenomenon that represents the functional connection of cortical areas (34–36).

Developments in functional MR imaging technology allow us to observe functional synchrony in the human brain through the analysis of temporally correlated SLF components present in the functional MR imaging signal. The sensitivity and temporal resolution of functional MR imaging make it an ideal modality for assessing functional synchrony. Biswal and colleagues observed that the low-frequency resting-state fluctuations from single-section functional MR imaging time series are correlated to the left and right regions of the motor cortex (13). Other research groups have repeated these results and demonstrated consistent findings (14,37). Furthermore, SLF components are sensitive to capillary blood flow and oxygenation (38). This observation of spatial-temporal correlation provides a basis for the COSLOF index to be used to assess functional synchrony in the human brain without a focal task.

Similar to SLF components observed with blood oxygen level–dependent functional MR imaging, early reports of the measurement of brain tissue PO2 with microelectrodes (39,40) prompted Cooper et al (39) to hypothesize that these oxygenation fluctuations play an important role in maintaining an optimum balance between cerebral blood flow and cerebral metabolic rate. This was considered the earliest effort to establish the neurophysiologic foundation of SLF. Findings in the Weliky and Katz study (41), with multielectrode recordings of neuronal action potential in the lateral geniculate nucleus of awake baby ferrets, revealed patterns of spontaneous activity. It has been demonstrated that each region of the nervous system can generate its own cyclical patterns that interact with those of the other regions to which it is interconnected (42,43). The most important finding by Weliky and Katz (41) is that although there is no direct connection that links both eyes, a significant cross-correlation coefficient exists between their spontaneous bursts of neuronal activity when corticothalamic feedback is intact. It has been directly established that the spatial and temporal properties of SLF have a neuronal foundation (44–46) and that synchrony of neuron firing may provide important information (47,48).

Neuropathologic changes are thought to begin in the hippocampal formation and become severe with disease progression (ie, the floor effect); thus, changes in the COSLOF index could reflect the earliest sign of AD in the region of the hippocampus. In this hypothesis, the hippocampus in the preclinical stage of AD is one of the first areas affected by neurofibrillary tangles and neuritic plaques, which in turn could affect functional synchrony, which can be measured and quantified with the COSLOF index. This characteristic of AD progression in the hippocampus region, together with the neuronal origin of SLF components and the high spatial and temporal resolutions of functional MR imaging detection, provide the foundation for the COSLOF index as a preclinical marker for the disease.

Unlike the compensatory response to functional stimuli in functional MR imaging or PET measurements, the COSLOF index has a unidirectional decrease with disease progression. In the present study, a wide range of individual COSLOF index values (from 0.05 to 0.46) and small intrasubject variation provide us with a powerful discrimination tool to characterize the different degrees of changes in functional synchrony among control subjects, MCI subjects, and AD patients. It is possible that the decrease in the COSLOF index reflects the degree of disruption in SLF synchrony in the brain of AD patients (49).

This property of the COSLOF index may fit the threshold theory for AD progression. Close inspection of Figure 4 reveals that the threshold of the COSLOF index, which separates the control group and the probable AD group, can be set around 0.19. We hypothesize that if a subject has a COSLOF index value in the hippocampus region that is less than 0.19, the subject may be at high risk of developing AD. This threshold of 0.19 may provide a quantitative evaluation for individuals with different amounts of brain reserve and with different degrees of an AD-type neuropathologic condition. Findings in numerous studies support a threshold model in which AD dementia is a result of the interplay between brain reserve and AD-type lesions (50). When the brain reserve is below a certain threshold, AD dementia will occur, and when the brain reserve remains above a certain threshold, AD dementia will not occur, even if many AD lesions are present. The COSLOF index may reflect a person’s remaining brain reserve and provide a possible explanation for why a large number of individuals who meet the neuropathologic criteria for AD after death are not cognitively impaired during life (51). This concept has been further supported and demonstrated by the results of a structural MR imaging study in which patients with smaller hippocampi and accelerated loss of temporal lobe volume developed dementia (52).

By applying the concept of threshold and the disconnection models of AD, the COSLOF index provides an opportunity to objectively study MCI subjects. By measuring the COSLOF index and its rate of change in MCI subjects, we may be able to identify those indexes that occur closer to the preclinical stage of AD. In the present study, there were two control subjects and four MCI subjects with COSLOF index values below the cut-off value of 0.19 who were judged to have high risk of developing AD. findings reveal If this judgment is found to be true at clinical follow-up, then the COSLOF index could be used as a preclinical marker for AD. With such a hypothesis, future prospective studies are needed in which the COSLOF index can be used to predict progression to AD in MCI subjects.

Our methods have technical advantages over typical functional MR brain mapping techniques. Because our functional MR procedures did not require patient participation in task-driven activities for cognitive paradigms, there was no performance-related head motion. Also, because the functional MR acquisition lasted 6 minutes or less, subject tolerance (a particular concern with the elderly) was substantially improved. Furthermore, because there were no cognitive demands for probable AD patients, compliance and tolerance for the experimental procedures were much greater than we have observed in cognitive functional MR imaging studies. Nevertheless, the high failure rate (nine of 33 [27%] subjects [four AD patients, one MCI subject, four control subjects]) due to head motion with this elderly population needs to be improved in future studies by either further constraining head movement or using real-time functional MR imaging techniques and immediately repeating the functional MR acquisition if head movement occurs.

A diagnosis of definite AD requires histopathologic confirmation. In our study, we could not provide histopathologic diagnosis for AD, and therefore we could not confirm a direct correlation between the COSLOF index and neuropathologic lesions in the hippocampus region. Nevertheless, the lack of postmortem verification in our study is mitigated by the high correlation of clinical and pathologic diagnosis (92%) at our institution (53); this percentage is similar to data reported from other national specialized dementia centers.

Finally, there are technical and biologic confounding factors that may affect the determination of the COSLOF index. For instance, the magnetic susceptibility artifacts near the air-tissue interfaces may affect the SLF measurement in the hippocampus region. Extensive data processing strategies must be implemented to reduce these artifacts (54). In addition, the temporal and spatial resolutions of voxel time courses obtained with different acquisition parameters may affect the COSLOF index determination. The reduction of cardiac aliasing effect with a longer repetition time must be performed to avoid steady-state free precession disturbance (24). Other factors that may alter the COSLOF index measurements include head motion and hippocampus atrophy. In addition, the lower COSLOF index measured in the region of the hippocampus will not absolutely indicate the progression of AD processes, although AD patients had lower COSLOF index values in our study. In other words, the lower COSLOF index may be necessary but not sufficient to mark AD, because other types of pathophysiology present in the hippocampus could also affect the COSLOF index value.

In conclusion, functional MR imaging technology can be applied to AD research and generate the COSLOF index to objectively quantify functional synchrony in the human hippocampus. The lower COSLOF index in AD patients may reflect the disruption of functional synchrony by AD lesions. Therefore, the COSLOF index may be a useful tool to distinguish between probable AD patients, MCI subjects, and cognitively healthy elderly subjects. The COSLOF index measured in the human hippocampus could be used as a noninvasive marker of the cumulative effects of AD and brain reserve.


   FOOTNOTES
 
Abbreviations: AD = Alzheimer disease, COSLOF = coefficients of spontaneous low frequency, GRE = gradient-recalled echo, MCI = mild cognitive impairment, MMSE = Mini-Mental Status Examination, SLF = spontaneous low-frequency fluctuation

Author contributions: Guarantor of integrity of entire study, S.J.L.; study concepts, S.J.L., Z.L., M.J.Z., P.G.A.; study design, S.J.L., Z.L., G.W., M.J.Z.; literature research, S.J.L., Z.L., G.W., M.J.Z.; clinical and experimental studies, Z.L.; data acquisition, S.J.L., Z.L., G.W.; data analysis/interpretation, S.J.L., Z.L., G.W., M.J.Z., P.G.A.; statistical analysis, S.J.L. G.W., M.J.Z.; manuscript preparation, S.J.L., Z.L., G.W., M.J.Z., P.G.A.; manuscript definition of intellectual content, S.J.L., M.F.; manuscript editing, S.J.L., Z.L., M.J.Z., M.F.; manuscript revision/review and final version approval, S.J.L., Z.L., G.W., M.J.Z., M.F., P.G.A.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 

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