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DELAYED SIGNAL TRANSMISSION IN AREA 17, AREA 18 AND THE POSTEROMEDIAL LATERAL SUPRASYLVIAN AREA OF AGED CATS Z. YAO, a Z. WANG, a N. YUAN, a Z. LIANG b AND Y. ZHOU a*

2009). Studies on cats have led to similar results (Hua et al., 2006). Signal timing is vital in the visual system. It has been suggested that synchronization based on latency contributes strongly to organizing the neuronal response encoding different objects (Gawne et al., 1996). Salthouse (1996) proposed the processing-speed theory in which he hypothesized that a decrease in processing speed is associated with increased age in adulthood, and this reduction in speed leads to impairments in cognitive function regarding the limited time mechanism and the simultaneity mechanism. Indeed, substantive experiments (Kline and Birren, 1975; Walsh, 1976; Walsh et al., 1979) on aging vision observed significant reduction in speed of vision processing, and this slowing contributes to higher order processing problems characteristic of cognitive aging (Salthouse, 1991a, 1993, 1994; Salthouse and Meinz, 1995). Thus, we have an interest in clarifying the effect of aging on signal timing. Previous studies have found that signal transmission delayed dramatically in V1 and V2 with more severe effects in V2 (Wang et al., 2005; Yu et al., 2006). However, no studies of age-related declines in rate of information processing were carried out in brain areas at higher levels than V2, especially such areas that specialized on motion (e.g., MT) or form (e.g., V4) information processing. Here, we examined the visual response latency of posteromedial lateral suprasylvian area (PMLS), which plays a similar role in MT in the cat visual system (Payne, 1993) for old and young cats. Because PMLS receives massive input from areas 17 and 18 and substantial input from thalamic nuclei (Dreher, 1986; Rauschecker et al., 1987), we also recorded in LGN, A17 and A18. It has been suggested that hyperactivity of brain cells during senescence results in the degradation of excitatory transmission (Olney et al., 1998; Perutz and Windle, 2001; Butterfield and Pocernich, 2003) and may correlate with degradation of signal timing; therefore, we also studied the spontaneous activity of cells.

a

CAS Key Laboratory of Brain Function and Diseases, and School of Life Sciences, University of Science and Technology of China, Hefei, China b Department of Bio-Medical Engineering, School of Life Science, Anhui Medical University, Hefei, China

Abstract—To investigate the effect of senescence on signal transmission, we have compared the visual response latency and spontaneous activity of cells in the lateral geniculate nucleus (LGN), area 17, area 18 and posteromedial lateral suprasylvian area (PMLS) of young and old cats. We found that LGN cells in old cats exhibit largely normal visual response latency. In contrast, all the other three areas exhibited significant aging-related delays in the visual response latency. On average, PMLS showed most pronounced delays among these three areas. Area 18 slowed more than area 17, but this was not significant. The degradation of signal timing in the visual cortex might provide insight into neuronal response mechanism underlying perception slowing during aging. Ó 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Key words: visual response latency, old cats, visual hierarchy, visual pathway, visual information processing, aging.

INTRODUCTION Normal aging is associated with decline in visual ability (Owsley, 2011). Much of the decline cannot be attributed to the optical changes in the eye or retina alteration (Spear, 1993; Norman et al., 2003). A significant amount of work has suggested that changes in the cortex during aging might be a more likely cause (Elliott, 1987; Mayer et al., 1988; Kim and Mayer, 1994; Elliott et al., 2009). Accordingly, electrophysiological studies on senescent monkeys found that neurons in the visual cortex, but not in the lateral geniculate nucleus (LGN), exhibit degradation in many aspects of response function, such as contrast sensitivity (Yang et al., 2008), orientation and direction selectivity (Schmolesky et al., 2000; Yu et al., 2006; Liang et al., 2010) and speed tuning (Yang et al.,

EXPERIMENTAL PROCEDURES Ethics statement This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of the University of

*Corresponding author. E-mail address: [email protected] (Y. Zhou). Abbreviations: CFH, cumulative frequency histogram; EF, expansion factor; LGN, lateral geniculate nucleus; LRS, lactated ringer’s solution; PMLS, posteromedial lateral suprasylvian area; PSTH, post-stimulus time histogram. http://dx.doi.org/10.1016/j.neuroscience.2015.01.004 0306-4522/Ó 2015 IBRO. Published by Elsevier Ltd. All rights reserved. 358

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Science and Technology of China (We cannot provide the permit number because our institution regulations do not require such numbers). Every effort was made to minimize suffering. Animal preparation All experiments were performed on four young, adult cats (1–3 years old) and four old cats (11–13 years old). Several lines of evidence have indicated that 12-monthold cats can be considered sexually mature while functional aging of the brain takes place in cats 10 years of age or older (Bowersox et al., 1984; Levine et al., 1986; Hua et al., 2006). The preparation for extracellular single-unit recording was carried out as previously described (Zhou and Baker, 1996). Cats were initially anesthetized with Ketamine HCl (20 mg/kg, i.m.) for venous cannulation, followed by propofol (6 mg/kg s.c.) during subsequent surgery. All pressure points and incision sites were treated with 1% lidocaine HCl and the corneas were protected with topical carboxymethylcellulose (1%). The heart rate was monitored and the rectal temperature was kept at 37.5 °C. After intravenous and tracheal cannulas were inserted, the cat was placed in a stereotaxic apparatus and a craniotomy was performed. When the surgery was completed, the animals were anesthetized by continuous infusion of propofol (5 mg/kg/h, i.v.) and sufentanil (10 ng/kg/h, i.v.). We assessed the level of anesthesia based on the status of the animals (e.g., reflex) and adjusted the speed of infusion to maintain vital signs within the appropriate range. After the status was stable, lactated ringer’s solution (LRS) with 2.5% dextrose was administered through a venous cannula (7–10 ml/kg/h) and gallamine triethiodide was delivered in the LRS solution (10 mg/kg/h) to maintain paralysis. Positive pressure ventilation (1:2 O2:N2O) was adjusted to maintain endtidal CO2 between 3.8% and 4.3%. Heart rate, ECG and EEG were monitored closely throughout the experiment to assess the level of anesthesia. We maintained the heart rate within the appropriate range. When the heart rate went out of range, we adjusted the rate of the propofol and sufentanil infusion. We also monitored the ECG and EEG rhythms for any irregular rhythm that might be caused by anesthesia. The animals received penicillin (2.5–5 mg/kg, s.c.) as prophylaxis against infection, dexamethasone (1.0–2.0 mg/kg, s.c.) to reduce cerebral edema and atropine (0.04 mg/kg, s.c.) to decrease tracheal secretions. Ophthalmic atropine (1%) and phenylephrine (10%) were instilled in the eyes to dilate the pupils and retract the nictitating membrane, respectively. The core temperature was maintained around 38 °C with a heating blanket (Harvard Apparatus). The cats were carefully examined ophthalmoscopically to rule out that no apparent optical or retinal problems that would impair visual functions. Retinal blood vessels, lens clarity and the maculae all seemed to be within normal limits. Appropriate contact lenses were used to protect the corneas. The optic disks of the two eyes were reflected on a tangent screen positioned 114 cm from the retina and the central area for both eyes were located. Spectacle lens were used as needed.

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Extracellular action potentials were recorded in LGN, A17, A18 and PMLS of young and old cats using epoxycoated, high-impedance (1–5 MX) tungsten electrodes (FHC). To reach the LGN, electrodes were lowered dorsoventrally through a craniotomy (Horsley–Clarke coordinates 9 mm lateral to the midline and 6 mm anterior to the ear bars). The LGN was identified by its stereotyped layer structure (the alternative of eye dominance) and the physiological responses of individual neurons (Zhou et al., 1995). Area 17 was targeted according to Horsley–Clarke coordinates, which were centered 2 mm lateral to the midline and 4 mm posterior to the ear bars. Area 18 was located through a craniotomy (Horsley–Clarke coordinates approximately 4 mm lateral to the midline and 3 mm anterior to the ear bars). For both areas 17 and 18, the electrode was advanced vertically. For PMLS a craniotomy of 0.8 cm was made at Horsley–Clarke coordinates A4–P4 and ±(L13–L21) and the electrode was advanced at an angle of 30° through an incision in the dura using penetrations perpendicular to the banks of the suprasylvian sulcus (Vajda et al., 2004). The order in which areas were studied was varied from animal to animal, thereby reducing the possible influence that could change neuronal response properties. Visual stimulation Flashing stimuli were generated by a computer and displayed monocularly on a gamma-corrected 19 inch CRT monitor (1024  768, 85 Hz; SONY, Tokyo, Japan) with a mean luminance of 19 cd/m2 using the psychophysics toolbox extensions (Brainard, 1997) for MATLAB (Mathworks, Natick, MA, USA). When a single unit was isolated, the cell’s classic receptive field was carefully mapped by a consecutively presenting series of computer-generated light spots on the monitor. We used flashing light spots as stimuli for LGN. The size of the light spot was set to be equal to the size of the center of the receptive field. The phase flicker stimulus was used to determine whether the neuron was of the X or Y type (Hochstein and Shapley, 1976). For the other three areas, we used flashing sinusoidal gratings with optimal parameters (size, spatial frequency, orientation and phase) as stimuli. The optimal size, spatial frequency and orientation of gratings were determined using drifting sinusoidal gratings and the optimal spatial phase was assessed by counter-flicker sinusoidal gratings. Each computer-generated flashing stimulus was presented 50 times with ON-period of 0.5 s and an OFF-period of 0.5 s. A blank screen with mean luminance was shown for 1 s before each stimulus presentation to obtain the baseline activities. There was a 5-s interval between trials for functional recovery. The Michelson contrast for each stimulus was set at 80%. The environment luminance on the cornea was 0.1 lx. Data collection and analysis After the response of an isolated cell was amplified with a microelectrode amplifier (Dagan, Minneapolis, MN, USA), signals were fed into a window discriminator (Winston Electronics, St. Louis, MO, USA) and audio monitor

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(Winston Electronics, St Louis, MO, USA). Raw signals were also digitized by a data acquisition board (National Instruments, Austin, TX, USA), controlled by IGOR software (WaveMetrics, Lake Oswego, OR, USA), and then saved for off-line analysis. For each cell, the poststimulus time histogram (PSTH) of response was drawn from the original data acquired in 50 repeated trials for further analysis. The response latency was determined through statistical analysis of spike train as previously described (Nicolelis et al., 1993). Briefly, the PSTH was converted to cumulative frequency histogram (CFH) and compared with a random distribution (Fig. 1). The probability that the overall distribution of CFH differs from a random distribution was computed using a one-way Kolmogorov–Smirnov test. The time at which the observed activity

Fig. 1. Example showing the method used for determining visually evoked onset latency. (A) The average PSTH derived from responses to repeated stimuli. Bin width was 1 ms. (B) Results of the statistical analysis for the original spike train (black solid) and two simulated spike trains (yellow and green dotted). EF indicates the expansion factor, which is the ratio of the simulated to original spontaneous activity (see details in Experimental procedures). Scales on the vertical axes of the plot show negative log p-values (for example at the first scale mark p = 0.1, at the second, p = 0.01 and so on). These show the probability that the overall distribution of cumulative frequencies, which were computed from PSTH mentioned above, differs from a random distribution, as computed using a one-way Kolmogorov–Smirnov test. The times at which the curve is above the black dotted line (p = 0.01) for the first time will be defined as the onset latency. Note that change of spontaneous activity affects the latency very little. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

exceeded 99% of the random distribution was considered the onset of response. Subtracting the baseline period, we obtained the visually evoked response latency, which indicates how long the response becomes significantly different from random since the beginning of stimuli. We chose a bin width of 1 ms to acquire high temporal resolution. The baseline activities were calculated as the average response during the 1-s period before the presentation of stimulus. To test how the latency calculated by this method was affected by the spontaneous activity, we simulated the process of calculation for representative cells and adjusted spontaneous activity by randomly adding or reducing a certain amount of spikes in the pre-period of spike trains (Fig. 1B). The expansion factor (EF) was defined as the ratio of the simulated to original spontaneous activity. EF equal to 1 indicates the original spike train (black solid, Fig. 1B). EF equal to 2 and 4 indicates the simulated train (green and yellow dotted, respectively, Fig. 1B), which means the simulated spontaneous activity is two and four times, respectively, over the original one. For the cell shown in Fig. 1B, varying the EF (from 1 to 4) could only make a difference in latency of approximately 2–3 ms. We repeated this simulation 50–100 times for each EF, and the resultant latencies only changed at most by 2 ms. The results of simulation showed that the spontaneous activity has negligible effects in determining the response latency. To estimate the range of neurons’ latencies in each brain area, bootstrap analyses were performed. For each brain area, we randomly re-sampled data from all cells in the brain area of old or young animals to create 1000 datasets that had the same sample size as the original dataset. Then, we determined the difference between the 30th percentile and the 90th percentile for each re-sampled datasets. Thus, we obtained 1000 estimates of ranges of neurons’ latency in each brain area for old and young cats. We were then able to estimate the mean and standard error of the ranges, and calculate the statistical significance. Cells in LGN were classified as either X- or Y-type using a modified ‘null position test’ (Hochstein and Shapley, 1976). All data in figures and text are expressed as mean ± SEM. Statistical analyses were performed using SPSS 13.0.

RESULTS The visual response latencies of 391 cells of old cats (11– 13 years old) and 330 cells of young cats (1–3 years old) were studied. Specifically, we recorded 85 PMLS cells, 134 A18 cells, 78 A17 cells and 94 LGN cells in old cats and 50 PMLS cells, 127 A18 cells, 57 A17 cells and 96 LGN cells in young cats. Neurons were recorded in the same range of depth from the surface of the brain to avoid laminar bias. All studied cells had receptive fields within 25° from projection of the area centralis and most were within 15°. No significant difference was found in the eccentricity distribution of neurons in these four brain areas between the young and old groups (p > 0.1

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for all brain areas, t-test). The onset latency of visually evoked activity was determined through statistical analysis of spike trains acquired in repeated 50 trials (see Experimental procedures). An example of PSTH and CFH plots used to determine the visual response onset latencies of a representative A18 young cell is shown in Fig. 1. Cells in LGN were classified as X cells and Y cells (Hochstein and Shapley, 1976). The proportions of X and Y cells were similar in young and old groups (75 X cells, 19 Y cells in the old group and 79 X cells, 17 Y cells in the young group, p > 0.6, v2 test). The average response latencies of X and Y cells in old and young cats are shown in Fig. 2. Consistent with previous results (Bolz et al., 1982; Long et al., 2008), Y cells exhibited a shorter latency than X cells in both young (23.8 ± 1.2 ms for X cells, 18.4 ± 0.9 ms for Y cells, p < 0.01, t-test) and old (24.4 ± 1.3 ms for X cells, 19.1 ± 1.1 ms for Y cells, p < 0.01, t-test) groups. X and Y cells in old cats exhibited similar latencies compared to young cats (p = 0.8 for X cells and p = 0.6 for Y cells, t-test). To clarify whether aging has a differential effect on X and Y cells further, we performed a two-way ANOVA analysis with aging (old/young) and cell type (X/Y) as factors. We observed a significant effect of cell type (p < 0.05) in the analysis. However, the effect of aging is not significant (p > 0.1), and there is no interaction between aging and cell type (p > 0.1). No differential effect of aging on the response latencies of X and Y cells was observed, and the purpose of this study is to examine aging effect; therefore, data from the two types of cells are combined for convenience in Table 1. As seen in Table 1, the LGN did not show a significant difference in response latencies between young and old cats (p > 0.1, t-test). Table 1 lists the mean visual response latencies of cells recorded from LGN, A17, A18 and PMLS of young and old cats. In contrast to the LGN in which onset response latencies of cells did not differ in young and old groups (p > 0.1, t-test), cells in A17, A18 and PMLS

Fig. 2. Comparison of the visual response onset latencies of X and Y cells between old and young cats. Y cells exhibited shorter latency than X cells in both young (23.8 ± 1.2 ms for X cells, 18.4 ± 0.9 ms for Y cells, p < 0.05, t-test) and old (24.4 ± 1.3 ms for X cells, 19.1 ± 1.1 ms for Y cells, p < 0.05, t-test) groups. X and Y cells in old cats have similar latencies to those in young cats (p = 0.8 for X cells and p = 0.6 for Y cells, t-test). Using a two-way ANOVA with aging and cell type as factors, we observed a significant effect of cell type (p < 0.05). However, the effect of aging is not significant (p > 0.1), and there is no interaction of aging and cell type (p > 0.1).

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showed much longer latencies in old than young cats (t-test, p < 0.001 in all cases). The difference in average latencies between old and young cats was largest in PMLS, followed by A18, and then A17. The percent increase in latencies was 19%, 25% and 37% in A17, A18 and PMLS, respectively. A two-way ANOVA analysis was conducted to examine the effect of aging and brain areas on visual response latencies. There was a significant interaction between the effects of aging and brain areas on visual response latency (p < 0.01). In addition, we performed a linear contrast of the latency means to test the interaction. The results showed that neurons in PMLS of old cats delayed more than neurons in A18 (p < 0.01) and A17 (p < 0.01), but neurons in A18 of old cats did not exhibit significant delay compared with neurons in A17 (p = 0.208). Fig. 3 shows the cumulative distribution for the visual response latencies of cells in A17, A18 in young and old cats. It is interesting that the cells with the shortest latencies in both young and old cats in areas A17 and A18 exhibited very similar latencies (Fig. 3A, B). Previous work (Alonso et al., 2001; Kropf et al., 2010) has shown that cells receiving direct input from LGN generally exhibit a shorter response latency to the visual stimuli. It is possible that the visual response latencies in cells from the input layer stay intact during aging, which still needs further investigation. For the remaining parts, the curve for old cats tended to shift rightward, which means that there were more cells that exhibited longer latencies in old cats. In addition, for A18 the curve shifted rightward in a more pronounced manner. In contrast to curves of A17 and A18, the curve for PLMS of old cats shifted rightward much more than the curve for young cats, and there was no overlap in the shortest latency part (Fig. 3C). The older group not only had larger average latencies, but also showed a wider range of latencies (Fig 3). For quantified analysis, the difference of the 30th percentile and 90th percentile of the curve was determined for A17, A18 and PMLS in both young and old cats. To obtain the mean and standard error, a bootstrap method was used (see Experimental procedures). For A17, the difference was 10.2 ± 1.3 ms in young cats and 27.5 ± 4.8 ms in old cats. For A18, the difference was 18.7 ± 2.7 ms in young cats and 37.1 ± 3 ms in old cats. For PMLS, short (30th percentile) and long (90th percentile) latency cells exhibited latencies that were separated by 17.9 ± 1.4 ms in young cats and 46.9 ± 4.6 ms in old ones. These differences for A17, A18 and PMLS are illustrated in Fig. 3D. As shown in Fig. 3D, the range of latencies increased in old cats with age (p < 0.01 for all brain areas, z-test). Importantly, PMLS that occupies the highest level among these brain areas exhibited the most pronounced increase in the range of latencies in old cats (p < 0.05, when compared with A17 or A18, z-test). However, the expansion in the range of latencies of cells in A17 is similar to that in A18 (p = 0.42, z-test). The choice of the percentile within a reasonable limit (i.e., 20th and 80th instead of 30th and 90th) did not affect this result. The more prominent increase in the range of latencies in PMLS over areas

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Table 1. Latency and spontaneous activity of cells recorded in LGN, A17, A18 and PMLS of old and young cats. Visual area

Young

Old

t-Value

Significance

Latency (ms)

LGN A17 A18 PMLS

23.0 ± 8.9 (n = 96) 35.7 ± 6.1 (n = 57) 42.4 ± 10.8 (n = 127) 54.0 ± 10.8 (n = 50)

24.4 ± 6.8 (n = 94) 42.6 ± 14.6 (n = 78) 53.0 ± 17.8 (n = 134) 74.4 ± 21.5 (n = 85)

1.166 3.741 5.859 7.332

p > 0.1 p < 0.001 p < 0.001 p < 0.001

Spontaneous activity (spikes/s)

LGN A17 A18 PMLS

12.4 ± 3.4 2.5 ± 2.1 1.4 ± 1.0 2.7 ± 2.4

13.6 ± 5.9 5.9 ± 3.2 3.7 ± 2.6 4.8 ± 2.8

1.786 7.569 9.76 4.292

p > 0.05 p < 0.001 p < 0.001 p < 0.001

(n = 96) (n = 57) (n = 127) (n = 50)

(n = 94) (n = 78) (n = 134) (n = 85)

Note that the latencies of LGN cells did not differ in old and young cats. However, the latencies of cells in A17, A18 and PMLS were significantly longer in old cats than in young cats. On average, the latencies of cells were 7 ms longer for A17, 11 ms longer for A18 and 20 ms longer for PMLS in old cats. The percent increase in latency is 6%, 19%, 25% and 37% from LGN to PMLS. Spontaneous activity of cells in LGN exhibited no significant difference in young and old cats. In contrast, old cells in the other three areas exhibited much higher spontaneous activities than young cells. The differences in spontaneous discharge rate were especially pronounced indicating that neurons in visual areas showing aging-related decline are continually firing at abnormally high rate.

Fig. 3. Latency of cells in A17, A18 and PMLS. (A–C) The percentage of cells with any given onset latency in A17, A18 and PMLS is shown in cumulative distribution plots, where solid and dotted lines represent the data of young and old cats, respectively. For the old cats, the curve shifted rightward which meant that old cats slowed more than young cats. Obviously, the curve for old cats in PMLS shifted more than in A17 and A18. This indicated that brain areas with higher levels might suffer more damage. (D) The range of latencies was calculated as the difference between 90th percentile and 30th percentile. The range of latencies within A17, A18 and PMLS is significantly greater in old than in young cats indicating that signal transfer among these areas takes longer in old cats. The especially larger increase in the range of latencies in old higher areas suggested that visual function declined in old cats and areas with higher level were affected more. The error bar represented the standard error, which was estimated from 1000 times of bootstrap simulations (see Experimental procedures).

17 and 18 indicated that PMLS function might be affected more than that in areas 17 and 18. Neuronal hyperactivity has been hypothesized to result in a degradation of excitatory transmission in a variety of brain disease (Olney et al., 1998; Perutz and Windle, 2001; Butterfield and Pocernich, 2003). Delayed information transfer in the old cat cortex is consistent with a degradation of excitatory transmission. Therefore, we related the spontaneous firing rates to the visual latencies of cells in LGN, A17, A18 and PMLS of old and young cats. Fig. 4B–D show that A17, A18 and PMLS cells in

old cats exhibited longer latencies as well as much higher spontaneous firing rates than did cells in young cats (t-test, p < 0.001 in all cases; Table 1). However, cells in LGN (Fig. 3A) of old and young cats did not significantly differ in latencies (t-test, p > 0.1; Table 1) and spontaneous firing rates (t-test, p > 0.05; Table 1). This was consistent with previous results (Wang et al., 2005; Wang et al., 2014). To analyze the relationship between visual response latency and spontaneous activity further, we determined the Pearson correlation coefficient (r) between these two measures for cells in each brain area

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Fig. 4. Scatter plots showing the spontaneous activities and visually evoked response onset latency of cells in LGN, A17, A18 and PMLS of old and young cats. (A) For LGN cells, the spontaneous activity and latency in old cats were similar to those in young cats. No significant difference was found. The visual response latency was not significantly related to the spontaneous activity in old (r = 0.187, p = 0.074; r is correlation coefficient) and young cats (r = 0.165, p = 0.106). (B–D) For the other three areas, compared with cells in young cats, cells in old cats exhibited longer latencies as well as greatly increased spontaneous responses. The visual response latency was also not significantly associated with the spontaneous activity in A17 (r = 0.206, p = 0.07, for old group; r = –0.235, p = 0.078, for young group), A18(r = 0.059, p = 0.496, for old group; r = -0.006, p = 0.944, for young group). Regarding PMLS, latency and spontaneous activity of cells of young cats were not significantly correlated (r = 0.245, p = 0.08), but there is significant correlation for cells of old cats (r = 0.389, p < 0.01). Response rates are in action potentials per second and latencies are in milliseconds.

of old and young cats. For young animals, the visual response latency was not significantly related to the spontaneous activity in LGN (r = 0.165, p = 0.106), A17 (r = –0.235, p = 0.078), A18 (r = –0.006, p = 0.944) and PMLS (r = 0.245, p = 0.08). For old animals, these two measures were also not significantly associated with each other in LGN (r = 0.187, p = 0.074), A17 (r = 0.206, p = 0.07) and A18 (r = 0.059, p = 0.496). However, cells with higher visual response latency in PMLS also have higher spontaneous activity (r = 0.389, p < 0.01).

DISCUSSION Here, we studied the latency differences among populations of cells in LGN, A17, A18 and PMLS of old and young cats. We found that the visual response latencies of cells in LGN were normal in old cats. However, cells in A17, A18 and PMLS on average displayed abnormally longer latencies in old cats. PMLS

exhibited the strongest aging effects while A17 showed the mildest ones. The range of latencies observed in A17, A18 and PMLS is larger in old cats and PMLS of old cats exhibited the largest range of latencies. Psychological studies have shown that slowing in visual processing speed is a common characteristic of aging, and has been well established as a phenomenon since the 1970s (Kline and Birren, 1975; Walsh, 1976; Walsh et al., 1979). Many older adults require more time than younger adults to detect, discriminate, recognize, or identify visual targets, and this slowing contributes to higher order processing problems characteristic of cognitive aging (e.g., associative learning, working memory, inhibition) (Salthouse, 1991b, 1993, 1994; Salthouse and Meinz, 1995). These deficits occur even in older adults who do not have conditions that cause dementia (e.g., Alzheimer’s disease, cerebrovascular accident). Similar function impairment was also reported in the nonhuman subjects of old monkeys (Rapp, 1990; Bachevalier et al., 1991; Voytko, 1999; Itoh et al., 2001). Importantly,

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Ball’s work has demonstrated that aging-related slowing in visual processing speed is exacerbated by increasing task demands (e.g., divided attention tasks) and by increasing visual clutter (e.g., distracting stimuli) (Ball et al., 2007). That is, in performing laboratory tasks, the display duration needed by many older adults to complete a task under dual task conditions with distracting stimuli is proportionately greater than what is needed by young adults. The progressively increased visual response latencies of neurons along the visual pathway presented here might provide substrates for such psychological observations. Several studies have also found that there is a larger effect of aging on the perception of higher order stimuli (Habak and Faubert, 2000; Tang and Zhou, 2009). Specifically, older adults’ perception threshold is elevated more for visually complex stimuli, i.e., second-order stimuli, compared with that for simpler stimuli, i.e., first-order stimuli (Habak and Faubert, 2000; Faubert, 2002; Tang and Zhou, 2009). The processing of complex stimuli required larger, more complex, and simultaneously engaged neural networks, so higher brain areas were considered to be responsible for the processing of complex stimuli (DeYoe and Van Essen, 1988; Faubert, 2002). The more severe degradation on response timing of higher brain areas during aging might be the neural basis for such psychological observations. Previous studies have shown that the properties and latencies of cells in the LGN of old monkeys are relatively normal (Spear et al., 1994). Here, we showed that the rate of information processing in the LGN of old cats is also not different from that in young cats. Moreover, the average latencies of cells with the shortest latencies in A17 and A18 were similar to that of cells in layer 4, which receives direct excitatory inputs from the LGN (Alonso et al., 2001; Kropf et al., 2010). This indicated that these cells might be located in the input layer. The similar latencies for these cells between the two age groups suggested that up to the input layer in A17 and A18, which receive direct inputs from the LGN, the effects of aging are not significant. This is consistent with previous work on monkeys (Wang et al., 2005), which reported that the latencies of cells locating in layer 4 of area V1 in old monkeys are similar to those in old monkeys. Neuronal morphological alterations have been reported frequently in the senescent human, nonhuman primate, rat and cat cortex (Masliah et al., 1993; Wong et al., 2000; Page et al., 2002; Zhang et al., 2011). A decrease in axonal conduction velocity and/or delayed synaptic transmission could contribute to the age-related latency increase. In fact, it has been reported that there is a degradation of myelinated fibers in area V1 and the prefrontal cortex that correlates with behavioral deficits (Peters, 2002; Peters and Sethares, 2002). Moreover, several studies have indicated that the dendrites and synapses, as well as the dendritic spines of V1 cells and corticocortical projection cells, degraded in old animals (Peters, 2002; Duan et al., 2003). Age-related changes in dendrites are of particular interest, because dendrites are the targets of the majority of synapses. For example, the decrease in dendritic branches and spines of neurons in the aged neocortex would result in a significant loss of

synaptic substrate, which in turn could influence temporal integration of synaptic inputs (Wong et al., 2000). Thus, age-related shifts in dendritic structures could also affect neuronal response latency. Excitotoxic hyperactivity has been hypothesized to result in failure of excitatory transmission in Alzheimer’s, Huntington’s, epilepsy and other brain disease through synaptic depression at an early stage and eventually cell death (Olney et al., 1998; Perutz and Windle, 2001; Butterfield and Pocernich, 2003). Dysfunction of the inhibitory system results in the disinhibition of neural circuitry, which in turn leads to the hyperactivity of the glutamate system. Leventhal et al. (2003) suggested that aging results in decreased GABAergic inhibition in the old monkey cortex and that reduced inhibition could account for much of the functional decline they observed. The old cells studied here exhibited abnormally high spontaneous activities, suggesting that the increased latencies we observed may reflect early failure of excitatory transmission induced by dysfunction of the inhibitory system. In addition, we observed that cells in senescent PMLS with higher spontaneous activity exhibited longer latency, while there was no significant relationship between these two measures in other cases, which might suggest that different mechanisms, one of which was hierarchy dependent, were involved in the hyperactivity during aging. This still needs further investigation. The direct excitatory inputs to A17 were most from LGN X cells. For A18, they stem from LGN Y cells and A17. A17, A18 and some brain areas (e.g., 21a) residing at higher positions in the hierarchy that constitutes the majority of excitatory inputs to PMLS (Yew et al., 1988; Sherk, 1990). Areas 17 and 18 in the cat receive direct input from the LGN; therefore, they are often thought of as constituting the equivalent of the primary visual cortex (Tretter et al., 1975; Raczkowski and Rosenquist, 1983; Sherk, 1986; Lomber et al., 1995). Consistently, our results showed that on average the effect of aging on A17 was not significantly larger than that on A18. In addition, the expansion in the range of latencies of cells in A17 is similar to that in A18. The massive excitatory inputs to PMLS are from A17 and A18, which have already been delayed in old cats, whereas the inputs to A17 and A18 from LGN did not differ between old and young cats. Thus, the cumulative effect may cause slowing of PMLS on average to a greater degree than A17 and A18 in old cats. However, we also observed that the range of latencies of cells in PMLS was increased more than that in A17 and A18 for old cats. This suggested that the intracortical processing in PMLS was affected more than that in A17 and A18. Age-related delay imposed on the afferent inputs to PMLS as well as the regularity of the afferent spike trains may have adverse effects on the precise coordination of the presynaptic spike discharge, which in turn will lead to the failure of action potential initiation (Magee, 2000; Leger et al., 2005; Spruston, 2008). In addition, it has been reported that the relative timing of retinal spike trains affects the efficacy of visual information processing in cortical neurons more than in LGN neurons (Kara and Reid, 2003). Indeed, we have observed more severe irregularities in

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spike trains of cells in A17, but not in LGN in old cats (Zhou et al., 2012). All this might give rise to the deferred intracortical processing in PMLS of old cats. It is possible that the neuronal morphology is altered more in higher order areas in old cats, but little has been done on this work so far. It is a concern that differential effects of anesthesia on cortical function in young and old cats could impact our results. This possibility has been previously examined in monkeys (Schmolesky et al., 2000; Leventhal et al., 2003; Wang et al., 2005) and cats (Hua et al., 2006), in both old and young animals, by recording the properties of individual cells while systematically varying anesthetic and paralytic levels. It has been found that giving as much as four times the minimum level of general anesthesia or paralysis required to anesthetize or paralyze both old and young animals does not alter the degree of selectivity for orientation and direction VI cells exhibit. Latency and spatial frequency sensitivity are also not changed in V1 by varying anesthesia or paralytic levels in young and old animals (Wang et al., 2005). In addition, a greater sensitivity of old animals to anesthesia is hard to reconcile with the finding that old cells exhibit higher spontaneity than do young cells. Thus, we conclude that problems with anesthesia in old animals are not a concern in our study. In summary, our results suggested that, overall, aging results in increases in latencies of cells in A17, A18 and PMLS in a way that is correlated with the position in which individual areas are located along the visual hierarchy. The degradation of signal timing in the visual cortex might provide a neuronal mechanism underlying the decline in visual ability during aging. Acknowledgments—This work was supported by National Natural Science Foundation of China (31230032 and 30970978), the National Basic Research Program of China (973 Program: 2009CB941303). The authors thank Dr. Guangxing Li and Dr. Shan Yu for comments on the manuscript and Mr. Mingjing Zhang for assistance in experiment.

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(Accepted 6 January 2015) (Available online 13 January 2015)